Mining High Utility Itemsets with Regular Occurrence

High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints

Literature Review on Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

This paper presenting a survey on finding itemsets with high utility. For finding itemsets there are many algorithms but those algorithms having a problem of producing a large number of candidate itemsets for high utility itemsets which reduces mining performance in terms of execution. Here we mainly focus on two algorithms utility pattern growth (UP-Growth) and UP-Growth+. Those algorithms are used for mining high utility itemsets, where effective methods are used for pruning candidate itemsets. Mining high utility itemsets Keep in a special data structure called UP-Tree. This, compact tree structure, UP-Tree, is used for make possible the mining performance and avoid scanning original database repeatedly. In this for generation of candidate itemsets only two scans of database. Another proposed algorithms UP Growth+ reduces the number of candidates effectively. It also has better performance than other algorithms in terms of runtime, especially when databases contain huge amount of long transactions. Utility-based data mining is a new research area which is interested in all types of utility factors in data mining processes. In which utility factors are targeted at integrate utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utility based descriptive data mining. Utility based data mining is used for finding itemsets that contribute most to the total utility in that database

Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments

In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since current basket-market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high-utility itemsets) in a large-scale database especially with IoT situations. First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases. The -means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3-tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR-Miner compared to the well-known and state-of-the-art CLS-Miner. We present our in-depth results to show that the developed GMR-Miner outperforms CLS-Miner in many criteria, i.e., memory usage, scalability, and runtime.publishedVersio

Implementation of an interactive pattern mining framework on electronic health record datasets

Large collections of electronic patient records contain a broad range of clinical information highly relevant for data analysis. However, they are maintained primarily for patient administration, and automated methods are required to extract valuable knowledge for predictive, preventive, personalized and participatory medicine. Sequential pattern mining is a fundamental task in data mining which can be used to find statistically relevant, non-trivial temporal dependencies of events such as disease comorbidities. This works objective is to use this mining technique to identify disease associations based on ICD-9-CM codes data of the entire Taiwanese population obtained from Taiwan’s National Health Insurance Research Database. This thesis reports the development and implementation of the Disease Pattern Miner – a pattern mining framework in a medical domain. The framework was designed as a Web application which can be used to run several state-of-the-art sequence mining algorithms on electronic health records, collect and filter the results to reduce the number of patterns to a meaningful size, and visualize the disease associations as an interactive model in a specific population group. This may be crucial to discover new disease associations and offer novel insights to explain disease pathogenesis. A structured evaluation of the data and models are required before medical data-scientist may use this application as a tool for further research to get a better understanding of disease comorbidities

Exploring Data Hierarchies to Discover Knowledge in Different Domains

L'abstract è presente nell'allegato / the abstract is in the attachmen

Exploring Decomposition for Solving Pattern Mining Problems

This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.publishedVersio

Discovering Periodic Itemsets Using Novel Periodicity Measures

Discovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions. Numerous methods can identify patterns exhibiting a periodic behavior. Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly. Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxPer (maximum periodicity) threshold. As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxPer constraint. Consequently, many patterns that are almost always periodic are not presented to the user. But these patterns could be considered as interesting as they generally appear periodically. To address this issue, this paper suggests to use three measures to identify periodic patterns. These measures are named average, maximum and minimum periodicity, respectively. They are each designed to evaluate a different aspect of the periodic behavior of patterns. By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements. The designed algorithm has been evaluated on several datasets. Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database

GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user\u27s interest is an important task. Existing works on patter

Improving E-Commerce Recommendations using High Utility Sequential Patterns of Historical Purchase and Click Stream Data

Recommendation systems not only aim to recommend products that suit the taste of consumers but also generate higher revenue and increase customer loyalty for e-commerce companies (such as Amazon, Netflix). Recommendation systems can be improved if user purchase behaviour are used to improve the user-item matrix input to Collaborative Filtering (CF). This matrix is mostly sparse as in real-life, a customer would have bought only very few products from the hundreds of thousands of products in the e-commerce shelf. Thus, existing systems like Kim11Rec, HPCRec18 and HSPRec19 systems use the customer behavior information to improve the accuracy of recommendations. Kim11Rec system used behavior and navigations patterns which were not used earlier. HPCRec18 system used purchase frequency and consequential bond between click and purchased data to improve the user-item frequency matrix. The HSPRec19 system converts historic click and purchase data to sequential data and enhances the user-item frequency matrix with the sequential pattern rules mined from the sequential data for input to the CF. HSPRec19 system generates recommendations based on frequent sequential purchase patterns and does not capture whether the recommended items are also of high utility to the seller (e.g., are more profitable?).The thesis proposes a system called High Utility Sequential Pattern Recommendation System (HUSRec System), which is an extension to the HSPRec19 system that replaces frequent sequential patterns with use of high utility sequential patterns. The proposed HUSRec generates a high utility sequential database from ACM RecSys Challenge dataset using the HUSDBG (High Utility Sequential Database Generator) and HUSPM (High Utility Sequential Pattern Miner) mines the high utility sequential pattern rules which can yield high sales profits for the seller based on quantity and price of items on daily basis, as they have at least the minimum sequence utility. This improves the accuracy of the recommendations. The proposed HUSRec mines clicks sequential data using PrefixSpan algorithm to give frequent sequential rules to suggest items where no purchase has happened, decreasing the sparsity of user-item matrix, improving the user-item matrix for input to the collaborative filtering. Experimental results with mean absolute error, precision and graphs show that the proposed HUSRec system provides more accurate recommendations and higher revenue than the tested existing systems. Keywords: Data mining, Sequential pattern mining, Collaborative filtering, High utility pattern mining, E-commerce recommendation systems

Similarity processing in multi-observation data

Many real-world application domains such as sensor-monitoring systems for environmental research or medical diagnostic systems are dealing with data that is represented by multiple observations. In contrast to single-observation data, where each object is assigned to exactly one occurrence, multi-observation data is based on several occurrences that are subject to two key properties: temporal variability and uncertainty. When defining similarity between data objects, these properties play a significant role. In general, methods designed for single-observation data hardly apply for multi-observation data, as they are either not supported by the data models or do not provide sufficiently efficient or effective solutions. Prominent directions incorporating the key properties are the fields of time series, where data is created by temporally successive observations, and uncertain data, where observations are mutually exclusive. This thesis provides research contributions for similarity processing - similarity search and data mining - on time series and uncertain data. The first part of this thesis focuses on similarity processing in time series databases. A variety of similarity measures have recently been proposed that support similarity processing w.r.t. various aspects. In particular, this part deals with time series that consist of periodic occurrences of patterns. Examining an application scenario from the medical domain, a solution for activity recognition is presented. Finally, the extraction of feature vectors allows the application of spatial index structures, which support the acceleration of search and mining tasks resulting in a significant efficiency gain. As feature vectors are potentially of high dimensionality, this part introduces indexing approaches for the high-dimensional space for the full-dimensional case as well as for arbitrary subspaces. The second part of this thesis focuses on similarity processing in probabilistic databases. The presence of uncertainty is inherent in many applications dealing with data collected by sensing devices. Often, the collected information is noisy or incomplete due to measurement or transmission errors. Furthermore, data may be rendered uncertain due to privacy-preserving issues with the presence of confidential information. This creates a number of challenges in terms of effectively and efficiently querying and mining uncertain data. Existing work in this field either neglects the presence of dependencies or provides only approximate results while applying methods designed for certain data. Other approaches dealing with uncertain data are not able to provide efficient solutions. This part presents query processing approaches that outperform existing solutions of probabilistic similarity ranking. This part finally leads to the application of the introduced techniques to data mining tasks, such as the prominent problem of probabilistic frequent itemset mining.Viele Anwendungsgebiete, wie beispielsweise die Umweltforschung oder die medizinische Diagnostik, nutzen Systeme der Sensorüberwachung. Solche Systeme müssen oftmals in der Lage sein, mit Daten umzugehen, welche durch mehrere Beobachtungen repräsentiert werden. Im Gegensatz zu Daten mit nur einer Beobachtung (Single-Observation Data) basieren Daten aus mehreren Beobachtungen (Multi-Observation Data) auf einer Vielzahl von Beobachtungen, welche zwei Schlüsseleigenschaften unterliegen: Zeitliche Veränderlichkeit und Datenunsicherheit. Im Bereich der Ähnlichkeitssuche und im Data Mining spielen diese Eigenschaften eine wichtige Rolle. Gängige Lösungen in diesen Bereichen, die für Single-Observation Data entwickelt wurden, sind in der Regel für den Umgang mit mehreren Beobachtungen pro Objekt nicht anwendbar. Der Grund dafür liegt darin, dass diese Ansätze entweder nicht mit den Datenmodellen vereinbar sind oder keine Lösungen anbieten, die den aktuellen Ansprüchen an Lösungsqualität oder Effizienz genügen. Bekannte Forschungsrichtungen, die sich mit Multi-Observation Data und deren Schlüsseleigenschaften beschäftigen, sind die Analyse von Zeitreihen und die Ähnlichkeitssuche in probabilistischen Datenbanken. Während erstere Richtung eine zeitliche Ordnung der Beobachtungen eines Objekts voraussetzt, basieren unsichere Datenobjekte auf Beobachtungen, die sich gegenseitig bedingen oder ausschließen. Diese Dissertation umfasst aktuelle Forschungsbeiträge aus den beiden genannten Bereichen, wobei Methoden zur Ähnlichkeitssuche und zur Anwendung im Data Mining vorgestellt werden. Der erste Teil dieser Arbeit beschäftigt sich mit Ähnlichkeitssuche und Data Mining in Zeitreihendatenbanken. Insbesondere werden Zeitreihen betrachtet, welche aus periodisch auftretenden Mustern bestehen. Im Kontext eines medizinischen Anwendungsszenarios wird ein Ansatz zur Aktivitätserkennung vorgestellt. Dieser erlaubt mittels Merkmalsextraktion eine effiziente Speicherung und Analyse mit Hilfe von räumlichen Indexstrukturen. Für den Fall hochdimensionaler Merkmalsvektoren stellt dieser Teil zwei Indexierungsmethoden zur Beschleunigung von ähnlichkeitsanfragen vor. Die erste Methode berücksichtigt alle Attribute der Merkmalsvektoren, während die zweite Methode eine Projektion der Anfrage auf eine benutzerdefinierten Unterraum des Vektorraums erlaubt. Im zweiten Teil dieser Arbeit wird die Ähnlichkeitssuche im Kontext probabilistischer Datenbanken behandelt. Daten aus Sensormessungen besitzen häufig Eigenschaften, die einer gewissen Unsicherheit unterliegen. Aufgrund von Mess- oder übertragungsfehlern sind gemessene Werte oftmals unvollständig oder mit Rauschen behaftet. In diversen Szenarien, wie beispielsweise mit persönlichen oder medizinisch vertraulichen Daten, können Daten auch nachträglich von Hand verrauscht werden, so dass eine genaue Rekonstruktion der ursprünglichen Informationen nicht möglich ist. Diese Gegebenheiten stellen Anfragetechniken und Methoden des Data Mining vor einige Herausforderungen. In bestehenden Forschungsarbeiten aus dem Bereich der unsicheren Datenbanken werden diverse Probleme oftmals nicht beachtet. Entweder wird die Präsenz von Abhängigkeiten ignoriert, oder es werden lediglich approximative Lösungen angeboten, welche die Anwendung von Methoden für sichere Daten erlaubt. Andere Ansätze berechnen genaue Lösungen, liefern die Antworten aber nicht in annehmbarer Laufzeit zurück. Dieser Teil der Arbeit präsentiert effiziente Methoden zur Beantwortung von Ähnlichkeitsanfragen, welche die Ergebnisse absteigend nach ihrer Relevanz, also eine Rangliste der Ergebnisse, zurückliefern. Die angewandten Techniken werden schließlich auf Problemstellungen im probabilistischen Data Mining übertragen, um beispielsweise das Problem des Frequent Itemset Mining unter Berücksichtigung des vollen Gehalts an Unsicherheitsinformation zu lösen