Evaluation and optimization of frequent association rule based classification

Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task

Distributed context discovering for predictive modeling

Click prediction has applications in various areas such as advertising, search and online sales. Usually user-intent information such as query terms and previous click history is used in click prediction. However, this information is not always available. For example, there are no queries from users on the webpages of content publishers, such as personal blogs. The available information for click prediction in this scenario are implicitly derived from users, such as visiting time and IP address. Thus, the existing approaches utilizing user-intent information may be inapplicable in this scenario; and the click prediction problem in this scenario remains unexplored to our knowledge. In addition, the challenges in handling skewed data streams also exist in prediction, since there is often a heavy traffic on webpages and few visitors click on them. In this thesis, we propose to use the pattern-based classification approach to tackle the click prediction problem. Attributes in webpage visits are combined by a pattern mining algorithm to enhance their power in prediction. To make the pattern-based classification handle skewed data streams, we adopt a sliding window to capture recent data, and an undersampling technique to handle the skewness. As a side problem raised by the pattern-based approach, mining patterns from large datasets is addressed by a distributed pattern sampling algorithm proposed by us. This algorithm shows its scalability in experiments. We validate our pattern-based approach in click prediction on a real-world dataset from a Dutch portal website. The experiments show our pattern-based approach can achieve an average AUC of 0.675 over a period of 36 days with a 5-day sized sliding window, which surpasses the baseline, a statically trained classification model without patterns by 0.002. Besides, the average weighted F-measure of our approach is 0.009 higher than the baseline. Therefore, our proposed approach can slightly improve classification performance; yet whether this improvement worth deployment in real scenarios remains a question. Click prediction has applications in various areas such as advertising, search and online sales. Usually user-intent information such as query terms and previous click history is used in click prediction. However, this information is not always available. For example, there are no queries from users on the webpages of content publishers, such as personal blogs. The available information for click prediction in this scenario are implicitly derived from users, such as visiting time and IP address. Thus, the existing approaches utilizing user-intent information may be inapplicable in this scenario; and the click prediction problem in this scenario remains unexplored to our knowledge. In addition, the challenges in handling skewed data streams also exist in prediction, since there is often a heavy traffic on webpages and few visitors click on them. In this thesis, we propose to use the pattern-based classification approach to tackle the click prediction problem. Attributes in webpage visits are combined by a pattern mining algorithm to enhance their power in prediction. To make the pattern-based classification handle skewed data streams, we adopt a sliding window to capture recent data, and an undersampling technique to handle the skewness. As a side problem raised by the pattern-based approach, mining patterns from large datasets is addressed by a distributed pattern sampling algorithm proposed by us. This algorithm shows its scalability in experiments. We validate our pattern-based approach in click prediction on a real-world dataset from a Dutch portal website. The experiments show our pattern-based approach can achieve an average AUC of 0.675 over a period of 36 days with a 5-day sized sliding window, which surpasses the baseline, a statically trained classification model without patterns by 0.002. Besides, the average weighted F-measure of our approach is 0.009 higher than the baseline. Therefore, our proposed approach can slightly improve classification performance; yet whether this improvement worth deployment in real scenarios remains a question

Generalised Interaction Mining: Probabilistic, Statistical and Vectorised Methods in High Dimensional or Uncertain Databases

Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, useful and ultimately understandable patterns in data. The core step of the KDD process is the application of Data Mining (DM) algorithms to efficiently find interesting patterns in large databases. This thesis concerns itself with three inter-related themes: Generalised interaction and rule mining; the incorporation of statistics into novel data mining approaches; and probabilistic frequent pattern mining in uncertain databases. An interaction describes an effect that variables have -- or appear to have -- on each other. Interaction mining is the process of mining structures on variables describing their interaction patterns -- usually represented as sets, graphs or rules. Interactions may be complex, represent both positive and negative relationships, and the presence of interactions can influence another interaction or variable in interesting ways. Finding interactions is useful in domains ranging from social network analysis, marketing, the sciences, e-commerce, to statistics and finance. Many data mining tasks may be considered as mining interactions, such as clustering; frequent itemset mining; association rule mining; classification rules; graph mining; flock mining; etc. Interaction mining problems can have very different semantics, pattern definitions, interestingness measures and data types. Solving a wide range of interaction mining problems at the abstract level, and doing so efficiently -- ideally more efficiently than with specialised approaches, is a challenging problem. This thesis introduces and solves the Generalised Interaction Mining (GIM) and Generalised Rule Mining (GRM) problems. GIM and GRM use an efficient and intuitive computational model based purely on vector valued functions. The semantics of the interactions, their interestingness measures and the type of data considered are flexible components of vectorised frameworks. By separating the semantics of a problem from the algorithm used to mine it, the frameworks allow both to vary independently of each other. This makes it easier to develop new methods by focusing purely on a problem's semantics and removing the burden of designing an efficient algorithm. By encoding interactions as vectors in the space (or a sub-space) of samples, they provide an intuitive geometric interpretation that inspires novel methods. By operating in time linear in the number of interesting interactions that need to be examined, the GIM and GRM algorithms are optimal. The use of GRM or GIM provides efficient solutions to a range of problems in this thesis, including graph mining, counting based methods, itemset mining, clique mining, a clustering problem, complex pattern mining, negative pattern mining, solving an optimisation problem, spatial data mining, probabilistic itemset mining, probabilistic association rule mining, feature selection and generation, classification and multiplication rule mining. Data mining is a hypothesis generating endeavour, examining large databases for patterns suggesting novel and useful knowledge to the user. Since the database is a sample, the patterns found should describe hypotheses about the underlying process generating the data. In searching for these patterns, a DM algorithm makes additional hypothesis when it prunes the search space. Natural questions to ask then, are: "Does the algorithm find patterns that are statistically significant?" and "Did the algorithm make significant decisions during its search?". Such questions address the quality of patterns found though data mining and the confidence that a user can have in utilising them. Finally, statistics has a range of useful tools and measures that are applicable in data mining. In this context, this thesis incorporates statistical techniques -- in particular, non-parametric significance tests and correlation -- directly into novel data mining approaches. This idea is applied to statistically significant and relatively class correlated rule based classification of imbalanced data sets; significant frequent itemset mining; mining complex correlation structures between variables for feature selection; mining correlated multiplication rules for interaction mining and feature generation; and conjunctive correlation rules for classification. The application of GIM or GRM to these problems lead to efficient and intuitive solutions. Frequent itemset mining (FIM) is a fundamental problem in data mining. While it is usually assumed that the items occurring in a transaction are known for certain, in many applications the data is inherently noisy or probabilistic; such as adding noise in privacy preserving data mining applications, aggregation or grouping of records leading to estimated purchase probabilities, and databases capturing naturally uncertain phenomena. The consideration of existential uncertainty of item(sets) makes traditional techniques inapplicable. Prior to the work in this thesis, itemsets were mined if their expected support is high. This returns only an estimate, ignores the probability distribution of support, provides no confidence in the results, and can lead to scenarios where itemsets are labeled frequent even if they are more likely to be infrequent. Clearly, this is undesirable. This thesis proposes and solves the Probabilistic Frequent Itemset Mining (PFIM) problem, where itemsets are considered interesting if the probability that they are frequent is high. The problem is solved under the possible worlds model and a proposed probabilistic framework for PFIM. Novel and efficient methods are developed for computing an itemset's exact support probability distribution and frequentness probability, using the Poisson binomial recurrence, generating functions, or a Normal approximation. Incremental methods are proposed to answer queries such as finding the top-k probabilistic frequent itemsets. A number of specialised PFIM algorithms are developed, with each being more efficient than the last: ProApriori is the first solution to PFIM and is based on candidate generation and testing. ProFP-Growth is the first probabilistic FP-Growth type algorithm and uses a proposed probabilistic frequent pattern tree (Pro-FPTree) to avoid candidate generation. Finally, the application of GIM leads to GIM-PFIM; the fastest known algorithm for solving the PFIM problem. It achieves orders of magnitude improvements in space and time usage, and leads to an intuitive subspace and probability-vector based interpretation of PFIM.Knowledge Discovery in Datenbanken (KDD) ist der nicht-triviale Prozess, gültiges, neues, potentiell nützliches und letztendlich verständliches Wissen aus großen Datensätzen zu extrahieren. Der wichtigste Schritt im KDD Prozess ist die Anwendung effizienter Data Mining (DM) Algorithmen um interessante Muster ("Patterns") in Datensätzen zu finden. Diese Dissertation beschäftigt sich mit drei verwandten Themen: Generalised Interaction und Rule Mining, die Einbindung von statistischen Methoden in neue DM Algorithmen und Probabilistic Frequent Itemset Mining (PFIM) in unsicheren Daten. Eine Interaktion ("Interaction") beschreibt den Einfluss, den Variablen aufeinander haben. Interaktionsmining ist der Prozess, Strukturen zwischen Variablen zu finden, die Interaktionsmuster beschreiben. Diese werden gewöhnlicherweise als Mengen, Graphen oder Regeln repräsentiert. Interaktionen können komplex sein und sowohl positive als auch negative Beziehungen repräsentieren. Außerdem kann das Vorhandensein von Interaktionen andere Interaktionen oder Variablen beeinflussen. Interaktionen stellen in Bereichen wie Soziale Netzwerk Analyse, Marketing, Wissenschaft, E-commerce, Statistik und Finanz wertvolle Information dar. Viele DM Methoden können als Interaktionsmining betrachtet werden: Zum Beispiel Clustering, Frequent Itemset Mining, Assoziationsregeln, Klassifikationsregeln, Graph Mining, Flock Mining, usw. Interaktionsmining-Probleme können sehr unterschiedliche Semantik, Musterdefinitionen, Interessantheitsmaße und Datentypen erfordern. Interaktionsmining-Probleme auf breiter und abstrakter Basis effizient -- und im Idealfall effizienter als mit spezialisierten Methoden -- zu lösen, ist ein herausforderndes Problem. Diese Dissertation führt das Generalised Interaction Mining (GIM) und das Generalised Rule Mining (GRM) Problem ein und beschreibt Lösungen für diese. GIM und GRM benutzen ein effizientes und intuitives Berechnungsmodell, das einzig und allein auf vektorbasierten Funktionen beruht. Die Semantik der Interaktionen, ihre Interessantheitsmaße und die Datenarten, sind Komponenten in vektorisierten Frameworks. Die Frameworks ermöglichen die Trennung der Problemsemantik vom Algorithmus, so dass beide unabhängig voneinander geändert werden können. Die Entwicklung neuer Methoden wird dadurch erleichtert, da man sich völlig auf die Problemsemantik fokussieren kann und sich nicht mit der Entwicklung problemspezifischer Algorithmen befassen muss. Die Kodierung der Interaktionen als Vektoren im gesamten Raum (oder Teilraum) der Stichproben stellt eine intuitive geometrische Interpretation dar, die neuartige Methoden inspiriert. Die GRM- und GIM- Algorithmen haben lineare Laufzeit in der Anzahl der Interaktionen die geprüft werden müssen und sind somit optimal. Die Anwendung von GRM oder GIM in dieser Dissertation ermöglicht effiziente Lösungen für eine Reihe von Problemen, wie zum Beispiel Graph Mining, Aufzählungsmethoden, Itemset Mining, Clique Mining, ein Clusteringproblem, das Finden von komplexen und negativen Mustern, die Lösung von Optimierungsproblemen, Spatial Data Mining, probabilistisches Itemset Mining, probabilistisches Mining von Assoziationsregel, Selektion und Erzeugung von Features, Mining von Klassifikations- und Multiplikationsregel, u.v.m. Data Mining ist ein Verfahren, das Hypothesen produziert, indem es in großen Datensätzen Muster findet und damit für den Anwender neues und nützliches Wissen vorschlägt. Da die untersuchte Datenbank ein Resultat des datenerzeugenden Prozesses ist, sollten die gefundenen Muster Erkenntnisse über diesen Prozess liefern. Bei der Suche nach diesen Mustern macht ein DM Algorithmus zusätzliche Hypothesen, wenn Teile des Suchraums ausgeschlossen werden. Die folgenden Fragen sind dabei wichtig: "Findet der Algorithmus statistisch signifikante Muster?" und "Hat der Algorithmus während des Suchprozesses signifikante Entscheidungen getroffen?". Diese Fragen beeinflussen die Qualität der Muster und die Sicherheit die der Anwender in ihrer Benutzung haben kann. Da die Statistik auch eine Reihe von nützlichen Methoden bereitstellt, die für DM anwendbar sind, kombiniert diese Dissertation einige statistische Methoden mit neuen DM Algorithmen, insbesondere nicht-parametrische Signifikanztests und Korrelation. Diese Idee wird für die folgenden Probleme angewandt: Signifikante und "relatively class correlated" regelbasierte Klassifikation in unsymmetrischen Datensätzen, signifikantes Frequent Itemset Mining, Mining von komplizierten Korrelationsstrukturen zwischen Variablen zum Zweck der Featureselektion, Mining von korrelierten Multiplikationsregeln zum Zwecke des Interaktionsminings und Featureerzeugung und konjunktive Korrelationsregeln für die Klassifikation. Die Anwendung von GIM und GRM auf diese Probleme führt zu effizienten und intuitiven Lösungen. Frequent Itemset Mining (FIM) ist ein fundamentales Problem im Data Mining. Obwohl allgemein die Annahme gilt, dass in einer Transaktion enthaltene Items bekannt sind, sind die Daten in vielen Anwendungen unsicher oder probabilistisch. Beispiele sind das Hinzufügen von Rauschen zu Datenschutzzwecken, die Gruppierung von Datensätzen die zu geschätzten Kaufwahrscheinlichkeiten führen und Datensätze deren Herkunft von Natur aus unsicher sind. Die Berücksichtigung von unsicheren Datensätzen verhindert die Anwendung von traditionellen Methoden. Vor der Arbeit in dieser Dissertation wurden Itemsets gesucht, deren erwartetes Vorkommen hoch ist. Diese Methode produziert jedoch nur Schätzwerte, vernachlässigt die Wahrscheinlichkeitsverteilung der Vorkommen, bietet keine Sicherheit für die Genauigkeit der Ergebnisse und kann zu Szenarien führen in denen das Vorkommen als häufig eingestuft wird, obwohl die Wahrscheinlichkeit höher ist, dass sie nur selten vorkommen. Solche Ergebnisse sind natürlich unerwünscht. Diese Dissertation führt das Probabilistic Frequent Itemset Mining (PFIM) ein. Diese Lösung betrachtet Itemsets als interessant, wenn die Wahrscheinlichkeit groß ist, dass sie häufig vorkommen. Die Problemlösung besteht aus der Anwendung des Possible Worlds Models und dem vorgeschlagenen probabilistisches Framework für PFIM. Es werden neue und effiziente Methoden entwickelt um die Wahrscheinlichkeitsverteilung des Vorkommens und die Häufigkeitsverteilung eines Itemsets zu berechnen. Dazu werden die Poisson Binomial Recurrence, Generating Functions, oder eine normalverteilte Annäherung verwendet. Inkrementelle Methoden werden vorgeschlagen um Fragen wie "Finde die top-k Probabilistic Frequent Itemsets" zu beantworten. Mehrere PFIM Algorithmen werden entwickelt, wobei die Effizienz von Algorithmus zu Algorithmus steigt: ProApriori ist die erste Lösung für PFIM und basiert auf erzeugen und testen von Kandidaten. ProFP-Growth ist der erste probabilistische FP-Growth Algorithmus. Er schlägt einen Probabilistic Frequent Pattern Tree (Pro-FPTree) vor, der Kandidatenerzeugung überflüssig macht. Die Anwendung von GIM führt schließlich zu GIM-PFIM, dem schnellsten bekannten Algorithmus zur Lösung des PFIM Problems. Dieser Algorithmus resultiert in einem um Größenordnungen besseren Zeit- und Speicherbedarf, und führt zu einer intuitiven Interpretation von PFIM, basierend auf Unterräumen und Wahrscheinlichkeitsvektoren

Generalised Interaction Mining: Probabilistic, Statistical and Vectorised Methods in High Dimensional or Uncertain Databases

Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, useful and ultimately understandable patterns in data. The core step of the KDD process is the application of Data Mining (DM) algorithms to efficiently find interesting patterns in large databases. This thesis concerns itself with three inter-related themes: Generalised interaction and rule mining; the incorporation of statistics into novel data mining approaches; and probabilistic frequent pattern mining in uncertain databases. An interaction describes an effect that variables have -- or appear to have -- on each other. Interaction mining is the process of mining structures on variables describing their interaction patterns -- usually represented as sets, graphs or rules. Interactions may be complex, represent both positive and negative relationships, and the presence of interactions can influence another interaction or variable in interesting ways. Finding interactions is useful in domains ranging from social network analysis, marketing, the sciences, e-commerce, to statistics and finance. Many data mining tasks may be considered as mining interactions, such as clustering; frequent itemset mining; association rule mining; classification rules; graph mining; flock mining; etc. Interaction mining problems can have very different semantics, pattern definitions, interestingness measures and data types. Solving a wide range of interaction mining problems at the abstract level, and doing so efficiently -- ideally more efficiently than with specialised approaches, is a challenging problem. This thesis introduces and solves the Generalised Interaction Mining (GIM) and Generalised Rule Mining (GRM) problems. GIM and GRM use an efficient and intuitive computational model based purely on vector valued functions. The semantics of the interactions, their interestingness measures and the type of data considered are flexible components of vectorised frameworks. By separating the semantics of a problem from the algorithm used to mine it, the frameworks allow both to vary independently of each other. This makes it easier to develop new methods by focusing purely on a problem's semantics and removing the burden of designing an efficient algorithm. By encoding interactions as vectors in the space (or a sub-space) of samples, they provide an intuitive geometric interpretation that inspires novel methods. By operating in time linear in the number of interesting interactions that need to be examined, the GIM and GRM algorithms are optimal. The use of GRM or GIM provides efficient solutions to a range of problems in this thesis, including graph mining, counting based methods, itemset mining, clique mining, a clustering problem, complex pattern mining, negative pattern mining, solving an optimisation problem, spatial data mining, probabilistic itemset mining, probabilistic association rule mining, feature selection and generation, classification and multiplication rule mining. Data mining is a hypothesis generating endeavour, examining large databases for patterns suggesting novel and useful knowledge to the user. Since the database is a sample, the patterns found should describe hypotheses about the underlying process generating the data. In searching for these patterns, a DM algorithm makes additional hypothesis when it prunes the search space. Natural questions to ask then, are: "Does the algorithm find patterns that are statistically significant?" and "Did the algorithm make significant decisions during its search?". Such questions address the quality of patterns found though data mining and the confidence that a user can have in utilising them. Finally, statistics has a range of useful tools and measures that are applicable in data mining. In this context, this thesis incorporates statistical techniques -- in particular, non-parametric significance tests and correlation -- directly into novel data mining approaches. This idea is applied to statistically significant and relatively class correlated rule based classification of imbalanced data sets; significant frequent itemset mining; mining complex correlation structures between variables for feature selection; mining correlated multiplication rules for interaction mining and feature generation; and conjunctive correlation rules for classification. The application of GIM or GRM to these problems lead to efficient and intuitive solutions. Frequent itemset mining (FIM) is a fundamental problem in data mining. While it is usually assumed that the items occurring in a transaction are known for certain, in many applications the data is inherently noisy or probabilistic; such as adding noise in privacy preserving data mining applications, aggregation or grouping of records leading to estimated purchase probabilities, and databases capturing naturally uncertain phenomena. The consideration of existential uncertainty of item(sets) makes traditional techniques inapplicable. Prior to the work in this thesis, itemsets were mined if their expected support is high. This returns only an estimate, ignores the probability distribution of support, provides no confidence in the results, and can lead to scenarios where itemsets are labeled frequent even if they are more likely to be infrequent. Clearly, this is undesirable. This thesis proposes and solves the Probabilistic Frequent Itemset Mining (PFIM) problem, where itemsets are considered interesting if the probability that they are frequent is high. The problem is solved under the possible worlds model and a proposed probabilistic framework for PFIM. Novel and efficient methods are developed for computing an itemset's exact support probability distribution and frequentness probability, using the Poisson binomial recurrence, generating functions, or a Normal approximation. Incremental methods are proposed to answer queries such as finding the top-k probabilistic frequent itemsets. A number of specialised PFIM algorithms are developed, with each being more efficient than the last: ProApriori is the first solution to PFIM and is based on candidate generation and testing. ProFP-Growth is the first probabilistic FP-Growth type algorithm and uses a proposed probabilistic frequent pattern tree (Pro-FPTree) to avoid candidate generation. Finally, the application of GIM leads to GIM-PFIM; the fastest known algorithm for solving the PFIM problem. It achieves orders of magnitude improvements in space and time usage, and leads to an intuitive subspace and probability-vector based interpretation of PFIM.Knowledge Discovery in Datenbanken (KDD) ist der nicht-triviale Prozess, gültiges, neues, potentiell nützliches und letztendlich verständliches Wissen aus großen Datensätzen zu extrahieren. Der wichtigste Schritt im KDD Prozess ist die Anwendung effizienter Data Mining (DM) Algorithmen um interessante Muster ("Patterns") in Datensätzen zu finden. Diese Dissertation beschäftigt sich mit drei verwandten Themen: Generalised Interaction und Rule Mining, die Einbindung von statistischen Methoden in neue DM Algorithmen und Probabilistic Frequent Itemset Mining (PFIM) in unsicheren Daten. Eine Interaktion ("Interaction") beschreibt den Einfluss, den Variablen aufeinander haben. Interaktionsmining ist der Prozess, Strukturen zwischen Variablen zu finden, die Interaktionsmuster beschreiben. Diese werden gewöhnlicherweise als Mengen, Graphen oder Regeln repräsentiert. Interaktionen können komplex sein und sowohl positive als auch negative Beziehungen repräsentieren. Außerdem kann das Vorhandensein von Interaktionen andere Interaktionen oder Variablen beeinflussen. Interaktionen stellen in Bereichen wie Soziale Netzwerk Analyse, Marketing, Wissenschaft, E-commerce, Statistik und Finanz wertvolle Information dar. Viele DM Methoden können als Interaktionsmining betrachtet werden: Zum Beispiel Clustering, Frequent Itemset Mining, Assoziationsregeln, Klassifikationsregeln, Graph Mining, Flock Mining, usw. Interaktionsmining-Probleme können sehr unterschiedliche Semantik, Musterdefinitionen, Interessantheitsmaße und Datentypen erfordern. Interaktionsmining-Probleme auf breiter und abstrakter Basis effizient -- und im Idealfall effizienter als mit spezialisierten Methoden -- zu lösen, ist ein herausforderndes Problem. Diese Dissertation führt das Generalised Interaction Mining (GIM) und das Generalised Rule Mining (GRM) Problem ein und beschreibt Lösungen für diese. GIM und GRM benutzen ein effizientes und intuitives Berechnungsmodell, das einzig und allein auf vektorbasierten Funktionen beruht. Die Semantik der Interaktionen, ihre Interessantheitsmaße und die Datenarten, sind Komponenten in vektorisierten Frameworks. Die Frameworks ermöglichen die Trennung der Problemsemantik vom Algorithmus, so dass beide unabhängig voneinander geändert werden können. Die Entwicklung neuer Methoden wird dadurch erleichtert, da man sich völlig auf die Problemsemantik fokussieren kann und sich nicht mit der Entwicklung problemspezifischer Algorithmen befassen muss. Die Kodierung der Interaktionen als Vektoren im gesamten Raum (oder Teilraum) der Stichproben stellt eine intuitive geometrische Interpretation dar, die neuartige Methoden inspiriert. Die GRM- und GIM- Algorithmen haben lineare Laufzeit in der Anzahl der Interaktionen die geprüft werden müssen und sind somit optimal. Die Anwendung von GRM oder GIM in dieser Dissertation ermöglicht effiziente Lösungen für eine Reihe von Problemen, wie zum Beispiel Graph Mining, Aufzählungsmethoden, Itemset Mining, Clique Mining, ein Clusteringproblem, das Finden von komplexen und negativen Mustern, die Lösung von Optimierungsproblemen, Spatial Data Mining, probabilistisches Itemset Mining, probabilistisches Mining von Assoziationsregel, Selektion und Erzeugung von Features, Mining von Klassifikations- und Multiplikationsregel, u.v.m. Data Mining ist ein Verfahren, das Hypothesen produziert, indem es in großen Datensätzen Muster findet und damit für den Anwender neues und nützliches Wissen vorschlägt. Da die untersuchte Datenbank ein Resultat des datenerzeugenden Prozesses ist, sollten die gefundenen Muster Erkenntnisse über diesen Prozess liefern. Bei der Suche nach diesen Mustern macht ein DM Algorithmus zusätzliche Hypothesen, wenn Teile des Suchraums ausgeschlossen werden. Die folgenden Fragen sind dabei wichtig: "Findet der Algorithmus statistisch signifikante Muster?" und "Hat der Algorithmus während des Suchprozesses signifikante Entscheidungen getroffen?". Diese Fragen beeinflussen die Qualität der Muster und die Sicherheit die der Anwender in ihrer Benutzung haben kann. Da die Statistik auch eine Reihe von nützlichen Methoden bereitstellt, die für DM anwendbar sind, kombiniert diese Dissertation einige statistische Methoden mit neuen DM Algorithmen, insbesondere nicht-parametrische Signifikanztests und Korrelation. Diese Idee wird für die folgenden Probleme angewandt: Signifikante und "relatively class correlated" regelbasierte Klassifikation in unsymmetrischen Datensätzen, signifikantes Frequent Itemset Mining, Mining von komplizierten Korrelationsstrukturen zwischen Variablen zum Zweck der Featureselektion, Mining von korrelierten Multiplikationsregeln zum Zwecke des Interaktionsminings und Featureerzeugung und konjunktive Korrelationsregeln für die Klassifikation. Die Anwendung von GIM und GRM auf diese Probleme führt zu effizienten und intuitiven Lösungen. Frequent Itemset Mining (FIM) ist ein fundamentales Problem im Data Mining. Obwohl allgemein die Annahme gilt, dass in einer Transaktion enthaltene Items bekannt sind, sind die Daten in vielen Anwendungen unsicher oder probabilistisch. Beispiele sind das Hinzufügen von Rauschen zu Datenschutzzwecken, die Gruppierung von Datensätzen die zu geschätzten Kaufwahrscheinlichkeiten führen und Datensätze deren Herkunft von Natur aus unsicher sind. Die Berücksichtigung von unsicheren Datensätzen verhindert die Anwendung von traditionellen Methoden. Vor der Arbeit in dieser Dissertation wurden Itemsets gesucht, deren erwartetes Vorkommen hoch ist. Diese Methode produziert jedoch nur Schätzwerte, vernachlässigt die Wahrscheinlichkeitsverteilung der Vorkommen, bietet keine Sicherheit für die Genauigkeit der Ergebnisse und kann zu Szenarien führen in denen das Vorkommen als häufig eingestuft wird, obwohl die Wahrscheinlichkeit höher ist, dass sie nur selten vorkommen. Solche Ergebnisse sind natürlich unerwünscht. Diese Dissertation führt das Probabilistic Frequent Itemset Mining (PFIM) ein. Diese Lösung betrachtet Itemsets als interessant, wenn die Wahrscheinlichkeit groß ist, dass sie häufig vorkommen. Die Problemlösung besteht aus der Anwendung des Possible Worlds Models und dem vorgeschlagenen probabilistisches Framework für PFIM. Es werden neue und effiziente Methoden entwickelt um die Wahrscheinlichkeitsverteilung des Vorkommens und die Häufigkeitsverteilung eines Itemsets zu berechnen. Dazu werden die Poisson Binomial Recurrence, Generating Functions, oder eine normalverteilte Annäherung verwendet. Inkrementelle Methoden werden vorgeschlagen um Fragen wie "Finde die top-k Probabilistic Frequent Itemsets" zu beantworten. Mehrere PFIM Algorithmen werden entwickelt, wobei die Effizienz von Algorithmus zu Algorithmus steigt: ProApriori ist die erste Lösung für PFIM und basiert auf erzeugen und testen von Kandidaten. ProFP-Growth ist der erste probabilistische FP-Growth Algorithmus. Er schlägt einen Probabilistic Frequent Pattern Tree (Pro-FPTree) vor, der Kandidatenerzeugung überflüssig macht. Die Anwendung von GIM führt schließlich zu GIM-PFIM, dem schnellsten bekannten Algorithmus zur Lösung des PFIM Problems. Dieser Algorithmus resultiert in einem um Größenordnungen besseren Zeit- und Speicherbedarf, und führt zu einer intuitiven Interpretation von PFIM, basierend auf Unterräumen und Wahrscheinlichkeitsvektoren

Associative classifier coupled with unsupervised feature reduction for dengue fever classification using gene expression data

Recent studies have established the potential of classifiers designed using association rule mining methods. The current study proposes such an associative classifier to efficiently detect dengue fever using gene expression data. Labelled gene expression data has been preprocessed and discretized to mine association rules using well-established rule mining methods. Thereafter, unsupervised clustering methods have been applied to the discretized gene expression data to reduce and select the most promising features. The final feature reduced discretized gene expression data is subsequently used to mine rules in order to classify subjects into 'Dengue Fever' or 'Healthy'. Two well-known association rule mining methods, viz., Apriori and FP-Growth, have been used here along with various types of well established clustering methods. Extensive analysis has been reported with performance parameters in terms of accuracy, precision, recall and false positive rate using 5-fold cross-validation. Furthermore, a separate investigation has been conducted to find the most suitable number of features and confidence of association rule mining methods. The experimental results obtained indicate accurate detection of dengue fever patients at an early stage using the proposed associative classification method.Web of Science10883538834

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.Funding for open access charge: Universidad de Granada / CBUA.ERDF and the Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20)ERDF and Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711)Spanish Ministry of Science and Innovation (grant number PID2019-107793GB-I00

Generating High Precision Classification Rules for Screening of Irrelevant Studies in Systematic Review Literature Searches

Systematic reviews aim to produce repeatable, unbiased, and comprehensive answers to clinical questions. Systematic reviews are an essential component of modern evidence based medicine, however due to the risks of omitting relevant research they are highly time consuming to create and are largely conducted manually. This thesis presents a novel framework for partial automation of systematic review literature searches. We exploit the ubiquitous multi-stage screening process by training the classifier using annotations made by reviewers in previous screening stages. Our approach has the benefit of integrating seamlessly with the existing screening process, minimising disruption to users. Ideally, classification models for systematic reviews should be easily interpretable by users. We propose a novel, rule based algorithm for use with our framework. A new approach for identifying redundant associations when generating rules is also presented. The proposed approach to redundancy seeks to both exclude redundant specialisations of existing rules (those with additional terms in their antecedent), as well as redundant generalisations (those with fewer terms in their antecedent). We demonstrate the ability of the proposed approach to improve the usability of the generated rules. The proposed rule based algorithm is evaluated by simulated application to several existing systematic reviews. Workload savings of up to 10% are demonstrated. There is an increasing demand for systematic reviews related to a variety of clinical disciplines, such as diagnosis. We examine reviews of diagnosis and contrast them against more traditional systematic reviews of treatment. We demonstrate existing challenges such as target class heterogeneity and high data imbalance are even more pronounced for this class of reviews. The described algorithm accounts for this by seeking to label subsets of non-relevant studies with high precision, avoiding the need to generate a high recall model of the minority class

Mining complex structured data: Enhanced methods and applications

Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Classification Using Association Rules

This research investigates the use of an unsupervised learning technique, association rules, to make class predictions. The use of association rules to make class predictions is a growing area of focus within data mining research. The research to date has focused predominately on balanced datasets or synthetized imbalanced datasets. There have been concerns raised that the algorithms using association rules to make classifications do not perform well on imbalanced datasets. This research comprehensively evaluates the accuracy of a number of association rule classifiers in predicting home loan sales in an Irish retail banking context. The experiments designed test three associative classifier algorithms CBA, CMAR and SPARCCC against two benchmark algorithms conditional inference trees and random forests on a naturally imbalanced dataset. The experiments implemented and evaluated show that the benchmark tree based algorithms conditional inference trees and random forests outperform the associative classifier models across a range of balanced accuracy measures. This research contributes to the growing body of research in extending association rules to make class prediction