MAPPING WITH THE HELP OF NEW PROPOSED ALGORITHM AND MODIFIED CLUSTER FORMATION ALGORITHM TO RECOMMEND AN ICE CREAM TO THE DIABETIC PATIENT BASED ON SUGAR CONTAIN IN IT

The research for suggesting an ice cream for a diabetic patient is carried out in data mining by using clustering and mapping between the data for ice cream and diabetic patients. Here, mapping of ice cream dataset with diabetic patient dataset is done by using MFCA, which is proposed and explained in this paper. The results obtained from MCFA algorithm and the new proposed algorithm are explained and verified and it is observed that they are having the relevance

Semantically-guided evolutionary knowledge discovery from texts

This thesis proposes a new approach for structured knowledge discovery from texts which considers both the mining process itself, the evaluation of this knowledge by the model, and the human assessment of the quality of the outcome.This is achieved by integrating Natural-Language technology and Genetic Algorithms to produce explanatory novel hypotheses. Natural-Language techniques are specifically used to extract genre-based information from text documents. Additional semantic and rhetorical information for generating training data and for feeding a semistructured Latent Semantic Analysis process is also captured.The discovery process is modeled by a semantically-guided Genetic Algorithm which uses training data to guide the search and optimization process. A number of novel criteria to evaluate the quality of the new knowledge are proposed. Consequently, new genetic operations suitable for text mining are designed, and techniques for Evolutionary Multi-Objective Optimization are adapted for the model to trade off between different criteria in the hypotheses.Domain experts were used in an experiment to assess the quality of the hypotheses produced by the model so as to establish their effectiveness in terms of novel and interesting knowledge. The assessment showed encouraging results for the discovered knowledge and for the correlation between the model and the human opinions

Quality and interestingness of association rules derived from data mining of relational and semi-structured data

Deriving useful and interesting rules from a data mining system are essential and important tasks. 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. As the data mining techniques are data-driven, it is beneficial to affirm the rules using a statistical approach. It is important to establish the ways in which the existing statistical measures and constraint parameters can be effectively utilized and the sequence of their usage.In this thesis, a systematic way to evaluate the association rules discovered from frequent, closed and maximal itemset mining algorithms; and frequent subtree mining algorithm including the rules based on induced, embedded and disconnected subtrees is presented. With reference to the frequent subtree mining, in addition a new direction is explored based on utilizing the DSM approach capable of preserving all information from tree-structured database in a flat data format, consequently enabling the direct application of a wider range of data mining analysis/techniques to tree-structured data. Implications of this approach were investigated and it was found that basing rules on disconnected subtrees, can be useful in terms of increasing the accuracy and the coverage rate of the rule set.A strategy that combines data mining and statistical measurement techniques such as sampling, redundancy and contradictive checks, correlation and regression analysis to evaluate the rules is developed. This framework is then applied to real-world datasets that represent diverse characteristics of data/items. Empirical results show that with a proper combination of data mining and statistical analysis, the proposed framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy rules. Moreover, the results reveal the important characteristics and differences between mining frequent, closed or maximal itemsets; and mining frequent subtree including the rules based on induced, embedded and disconnected subtrees; as well as the impact of confidence measure for the prediction and classification task

Pattern mining under different conditions

New requirements and demands on pattern mining arise in modern applications, which cannot be fulfilled using conventional methods. For example, in scientific research, scientists are more interested in unknown knowledge, which usually hides in significant but not frequent patterns. However, existing itemset mining algorithms are designed for very frequent patterns. Furthermore, scientists need to repeat an experiment many times to ensure reproducibility. A series of datasets are generated at once, waiting for clustering, which can contain an unknown number of clusters with various densities and shapes. Using existing clustering algorithms is time-consuming because parameter tuning is necessary for each dataset. Many scientific datasets are extremely noisy. They contain considerably more noises than in-cluster data points. Most existing clustering algorithms can only handle noises up to a moderate level. Temporal pattern mining is also important in scientific research. Existing temporal pattern mining algorithms only consider pointbased events. However, most activities in the real-world are interval-based with a starting and an ending timestamp. This thesis developed novel pattern mining algorithms for various data mining tasks under different conditions. The first part of this thesis investigates the problem of mining less frequent itemsets in transactional datasets. In contrast to existing frequent itemset mining algorithms, this part focus on itemsets that occurred not that frequent. Algorithms NIIMiner, RaCloMiner, and LSCMiner are proposed to identify such kind of itemsets efficiently. NIIMiner utilizes the negative itemset tree to extract all patterns that occurred less than a given support threshold in a top-down depth-first manner. RaCloMiner combines existing bottom-up frequent itemset mining algorithms with a top-down itemset mining algorithm to achieve a better performance in mining less frequent patterns. LSCMiner investigates the problem of mining less frequent closed patterns. The second part of this thesis studied the problem of interval-based temporal pattern mining in the stream environment. Interval-based temporal patterns are sequential patterns in which each event is aligned with a starting and ending temporal information. The ability to handle interval-based events and stream data is lacking in existing approaches. A novel intervalbased temporal pattern mining algorithm for stream data is described in this part. The last part of this thesis studies new problems in clustering on numeric datasets. The first problem tackled in this part is shape alternation adaptivity in clustering. In applications such as scientific data analysis, scientists need to deal with a series of datasets generated from one experiment. Cluster sizes and shapes are different in those datasets. A kNN density-based clustering algorithm, kadaClus, is proposed to provide the shape alternation adaptability so that users do not need to tune parameters for each dataset. The second problem studied in this part is clustering in an extremely noisy dataset. Many real-world datasets contain considerably more noises than in-cluster data points. A novel clustering algorithm, kenClus, is proposed to identify clusters in arbitrary shapes from extremely noisy datasets. Both clustering algorithms are kNN-based, which only require one parameter k. In each part, the efficiency and effectiveness of the presented techniques are thoroughly analyzed. Intensive experiments on synthetic and real-world datasets are conducted to show the benefits of the proposed algorithms over conventional approaches

Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities

While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-experts are necessary for user trust and human-AI collaboration, the majority of explanation methods for AI are focused on developers and expert users. Counterfactual explanations are local explanations that offer users advice on what can be changed in the input for the output of the black-box model to change. Counterfactuals are user-friendly and provide actionable advice for achieving the desired output from the AI system. While extensively researched in supervised learning, there are few methods applying them to reinforcement learning (RL). In this work, we explore the reasons for the underrepresentation of a powerful explanation method in RL. We start by reviewing the current work in counterfactual explanations in supervised learning. Additionally, we explore the differences between counterfactual explanations in supervised learning and RL and identify the main challenges that prevent the adoption of methods from supervised in reinforcement learning. Finally, we redefine counterfactuals for RL and propose research directions for implementing counterfactuals in RL.Comment: 32 pages, 6 figure

Integer optimization methods for machine learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 129-137).In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks in diverse domains such as information retrieval, recommender systems, natural language processing, bioinformatics, and preventative maintenance. The primary advantage of using MIO for ranking is that it allows for direct optimization of ranking quality measures, as opposed to current state-of-the-art algorithms that use heuristic loss functions. We demonstrate using a number of datasets that our approach can outperform other ranking methods. The second part of the thesis focuses on reverse-engineering ranking models. This is an application of a more general ranking problem than the bipartite case. Quality rankings affect business for many organizations, and knowing the ranking models would allow these organizations to better understand the standards by which their products are judged and help them to create higher quality products. We introduce an MIO method for reverse-engineering such models and demonstrate its performance in a case-study with real data from a major ratings company. We also devise an approach to find the most cost-effective way to increase the rank of a certain product. In the final part of the thesis, we develop MIO methods to first generate association rules and then use the rules to build an interpretable classifier in the form of a decision list, which is an ordered list of rules. These are both combinatorially challenging problems because even a small dataset may yield a large number of rules and a small set of rules may correspond to many different orderings. We show how to use MIO to mine useful rules, as well as to construct a classifier from them. We present results in terms of both classification accuracy and interpretability for a variety of datasets.by Allison An Chang.Ph.D

The Efficient Discovery of Interesting Closed Pattern Collections

Enumerating closed sets that are frequent in a given database is a fundamental data mining technique that is used, e.g., in the context of market basket analysis, fraud detection, or Web personalization. There are two complementing reasons for the importance of closed sets---one semantical and one algorithmic: closed sets provide a condensed basis for non-redundant collections of interesting local patterns, and they can be enumerated efficiently. For many databases, however, even the closed set collection can be way too large for further usage and correspondingly its computation time can be infeasibly long. In such cases, it is inevitable to focus on smaller collections of closed sets, and it is essential that these collections retain both: controlled semantics reflecting some notion of interestingness as well as efficient enumerability. This thesis discusses three different approaches to achieve this: constraint-based closed set extraction, pruning by quantifying the degree or strength of closedness, and controlled random generation of closed sets instead of exhaustive enumeration. For the original closed set family, efficient enumerability results from the fact that there is an inducing efficiently computable closure operator and that its fixpoints can be enumerated by an amortized polynomial number of closure computations. Perhaps surprisingly, it turns out that this connection does not generally hold for other constraint combinations, as the restricted domains induced by additional constraints can cause two things to happen: the fixpoints of the closure operator cannot be enumerated efficiently or an inducing closure operator does not even exist. This thesis gives, for the first time, a formal axiomatic characterization of constraint classes that allow to efficiently enumerate fixpoints of arbitrary closure operators as well as of constraint classes that guarantee the existence of a closure operator inducing the closed sets. As a complementary approach, the thesis generalizes the notion of closedness by quantifying its strength, i.e., the difference in supporting database records between a closed set and all its supersets. This gives rise to a measure of interestingness that is able to select long and thus particularly informative closed sets that are robust against noise and dynamic changes. Moreover, this measure is algorithmically sound because all closed sets with a minimum strength again form a closure system that can be enumerated efficiently and that directly ties into the results on constraint-based closed sets. In fact both approaches can easily be combined. In some applications, however, the resulting set of constrained closed sets is still intractably large or it is too difficult to find meaningful hard constraints at all (including values for their parameters). Therefore, the last part of this thesis presents an alternative algorithmic paradigm to the extraction of closed sets: instead of exhaustively listing a potentially exponential number of sets, randomly generate exactly the desired amount of them. By using the Markov chain Monte Carlo method, this generation can be performed according to any desired probability distribution that favors interesting patterns. This novel randomized approach complements traditional enumeration techniques (including those mentioned above): On the one hand, it is only applicable in scenarios that do not require deterministic guarantees for the output such as exploratory data analysis or global model construction. On the other hand, random closed set generation provides complete control over the number as well as the distribution of the produced sets.Das Aufzählen abgeschlossener Mengen (closed sets), die häufig in einer gegebenen Datenbank vorkommen, ist eine algorithmische Grundaufgabe im Data Mining, die z.B. in Warenkorbanalyse, Betrugserkennung oder Web-Personalisierung auftritt. Die Wichtigkeit abgeschlossener Mengen ist semantisch als auch algorithmisch begründet: Sie bilden eine nicht-redundante Basis zur Erzeugung von lokalen Mustern und können gleichzeitig effizient aufgezählt werden. Allerdings kann die Anzahl aller abgeschlossenen Mengen, und damit ihre Auflistungszeit, das Maß des effektiv handhabbaren oft deutlich übersteigen. In diesem Fall ist es unvermeidlich, kleinere Ausgabefamilien zu betrachten, und es ist essenziell, dass dabei beide o.g. Eigenschaften erhalten bleiben: eine kontrollierte Semantik im Sinne eines passenden Interessantheitsbegriffes sowie effiziente Aufzählbarkeit. Diese Arbeit stellt dazu drei Ansätze vor: das Einführen zusätzlicher Constraints, die Quantifizierung der Abgeschlossenheit und die kontrollierte zufällige Erzeugung einzelner Mengen anstelle von vollständiger Aufzählung. Die effiziente Aufzählbarkeit der ursprünglichen Familie abgeschlossener Mengen rührt daher, dass sie durch einen effizient berechenbaren Abschlussoperator erzeugt wird und dass desweiteren dessen Fixpunkte durch eine amortisiert polynomiell beschränkte Anzahl von Abschlussberechnungen aufgezählt werden können. Wie sich herausstellt ist dieser Zusammenhang im Allgemeinen nicht mehr gegeben, wenn die Funktionsdomäne durch Constraints einschränkt wird, d.h., dass die effiziente Aufzählung der Fixpunkte nicht mehr möglich ist oder ein erzeugender Abschlussoperator unter Umständen gar nicht existiert. Diese Arbeit gibt erstmalig eine axiomatische Charakterisierung von Constraint-Klassen, die die effiziente Fixpunktaufzählung von beliebigen Abschlussoperatoren erlauben, sowie von Constraint-Klassen, die die Existenz eines erzeugenden Abschlussoperators garantieren. Als ergänzenden Ansatz stellt die Dissertation eine Generalisierung bzw. Quantifizierung des Abgeschlossenheitsbegriffs vor, der auf der Differenz zwischen den Datenbankvorkommen einer Menge zu den Vorkommen all seiner Obermengen basiert. Mengen, die bezüglich dieses Begriffes stark abgeschlossen sind, weisen eine bestimmte Robustheit gegen Veränderungen der Eingabedaten auf. Desweiteren wird die gewünschte effiziente Aufzählbarkeit wiederum durch die Existenz eines effizient berechenbaren erzeugenden Abschlussoperators sichergestellt. Zusätzlich zu dieser algorithmischen Parallele zum Constraint-basierten Vorgehen, können beide Ansätze auch inhaltlich kombiniert werden. In manchen Anwendungen ist die Familie der abgeschlossenen Mengen, zu denen die beiden oben genannten Ansätze führen, allerdings immer noch zu groß bzw. ist es nicht möglich, sinnvolle harte Constraints und zugehörige Parameterwerte zu finden. Daher diskutiert diese Arbeit schließlich noch ein völlig anderes Paradigma zur Erzeugung abgeschlossener Mengen als vollständige Auflistung, nämlich die randomisierte Generierung einer Anzahl von Mengen, die exakt den gewünschten Vorgaben entspricht. Durch den Einsatz der Markov-Ketten-Monte-Carlo-Methode ist es möglich die Verteilung dieser Zufallserzeugung so zu steuern, dass das Ziehen interessanter Mengen begünstigt wird. Dieser neue Ansatz bildet eine sinnvolle Ergänzung zu herkömmlichen Techniken (einschließlich der oben genannten): Er ist zwar nur anwendbar, wenn keine deterministischen Garantien erforderlich sind, erlaubt aber andererseits eine vollständige Kontrolle über Anzahl und Verteilung der produzierten Mengen

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