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

Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns

Understanding customer buying patterns is of great interest to the retail industry and has shown to benefit a wide variety of goals ranging from managing stocks to implementing loyalty programs. Association rule mining is a common technique for extracting correlations such as "people in the South of France buy ros\'e wine" or "customers who buy pat\'e also buy salted butter and sour bread." Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular products in the top rules. As a result, a number of "interestingness" measures (over 30) have been proposed to rank rules. However, there is no agreement on which measures are more appropriate for retail data. Moreover, since pattern mining algorithms output thousands of association rules for each product, the ability for an analyst to rely on ranking measures to identify the most interesting ones is crucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), a framework that provides analysts with the ability to compare the outcome of interestingness measures applied to buying patterns in the retail industry. We report on how we used CAPA to compare 34 measures applied to over 1,800 stores of Intermarch\'e, one of the largest food retailers in France

Statistical strategies for pruning all the uninteresting association rules

We propose a general framework to describe formally the problem of capturing the intensity of implication for association rules through statistical metrics. In this framework we present properties that influence the interestingness of a rule, analyze the conditions that lead a measure to perform a perfect prune at a time, and define a final proper order to sort the surviving rules. We will discuss why none of the currently employed measures can capture objective interestingness, and just the combination of some of them, in a multi-step fashion, can be reliable. In contrast, we propose a new simple modification of the Pearson coefficient that will meet all the necessary requirements. We statistically infer the convenient cut-off threshold for this new metric by empirically describing its distribution function through simulation. Final experiments serve to show the ability of our proposal.Postprint (published version

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Cataloged from PDF version of article.In a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the last month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrive, the association rule learning algorithm is executed on the last set of transactions, resulting in new association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as "association rule set stream" or "streaming association rules" and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to another and also over time for a given expert. This paper proposes a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors related to the interestingness of the association rules, and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real market dataset. The results show that the model can successfully select the interesting ones. (C) 2008 Elsevier B.V. All rights reserved

A framework for an Integrated Mining of Heterogeneous data in decision support systems

The volume of information available on the Internet and corporate intranets continues to increase along with the corresponding increase in the data (structured and unstructured) stored by many organizations. Over the past years, data mining techniques have been used to explore large volume of data (structured) in order to discover knowledge, often in form of a decision support system. For effective decision making, there is need to discover knowledge from both structured and unstructured data for completeness and comprehensiveness. The aim of this paper is to present a framework to discover this kind of knowledge and to present a report on the work-in-progress on an on going research work. The proposed framework is composed of three basic phases: extraction and integration, data mining and finally the relevance of such a system to the business decision support system. In the first phase, both the structured and unstructured data are combined to form an XML database (combined data warehouse (CDW)). Efficiency is enhanced by clustering of unstructured data (documents) using SOM (Self Organized Maps) clustering algorithm, extracting keyphrases based on training and TF/IDF (Term Frequency/Inverse Document Frequency) by using the KEA (Keyphrases Extraction Algorithm) toolkit. In the second phase, association rule mining technique is applied to discover knowledge from the combined data warehouse. The final phase reflects the changes that such a system will bring about to the marketing decision support system. The paper also describes a developed system which evaluates the association rules mined from structured data that forms the first phase of the research work. The proposed system is expected to improve the quality of decisions, and this will be evaluated by using standard metrics for evaluating the interestingness of association rule which is based on statistical independence and correlation analysis

Text mining with exploitation of user\u27s background knowledge : discovering novel association rules from text

The goal of text mining is to find interesting and non-trivial patterns or knowledge from unstructured documents. Both objective and subjective measures have been proposed in the literature to evaluate the interestingness of discovered patterns. However, objective measures alone are insufficient because such measures do not consider knowledge and interests of the users. Subjective measures require explicit input of user expectations which is difficult or even impossible to obtain in text mining environments. This study proposes a user-oriented text-mining framework and applies it to the problem of discovering novel association rules from documents. The developed system, uMining, consists of two major components: a background knowledge developer and a novel association rules miner. The background knowledge developer learns a user\u27s background knowledge by extracting keywords from documents already known to the user (background documents) and developing a concept hierarchy to organize popular keywords. The novel association rule miner discovers association rules among noun phrases extracted from relevant documents (target documents) and compares the rules with the background knowledge to predict the rule novelty to the particular user (useroriented novelty). The user-oriented novelty measure is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. It consists of two components: occurrence distance and connection distance. The former considers the co-occurrences of two keywords in the background documents: the more the shorter the distance. The latter considers the common connections of with others in the concept hierarchy. It is defined as the length of the connecting the two keywords in the concept hierarchy: the longer the path, distance. The user-oriented novelty measure is evaluated from two perspectives: novelty prediction accuracy and usefulness indication power. The results show that the useroriented novelty measure outperforms the WordNet novelty measure and the compared objective measures in term of predicting novel rules and identifying useful rules

Learning what matters - Sampling interesting patterns

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods.Comment: PAKDD 2017, extended versio