By applying the concept of domaindriven data mining, we repeatedly utilize decision trees and interestingness measures in a closedloop, indepth mining process to find unexpected and interesting patterns. Good measures also allow the time and space costs of the mining process to be reduced. Data mining and concepts and techniques, by jiawei han and micheline kamber. Standardizing interestingness measures for association. Consider any association rule mining algorithm, apriori or fp tree. Part of their analysis includes identifying interestingness measures that satisfied the three properties of piatetskyshapiro as well as the five additional properties. The agreement of such measures with a statistically sound significant dependency between the evidence and the hypothesis in data is. Probability based objective interestingness measures reference. I have been unable to find a pdf version online, but the reference is r. Pdf it is a wellknown fact that the data mining process can generate many hundreds and often thousands of patterns from data.
A very important aspect of data mining research is the determination of how interesting a pattern is. The fourth quality issues, measures of interestingness and evaluation of data mining models workshop qimie15 will focus on these questions and should be of great interest for a large panel of data miners. Quality measures in data mining fabrice guillet springer. Clustering is a division of data into groups of similar objects. In this paper, we aim to formalize the data mining process. Section four concentrates on objective interestingness measures. The measures are used to determine one pattern is more interesting than another.
Associative classification is a rulebased approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. The definition of quality of association rules is a well studied topic in statistics and data mining. Measures of patterns interestingness whether subjective or objective. Association strives to discover patterns in data which are based upon relationships between items in the same transaction. These evaluation measures can be used to rank groups of individuals and also rules within each group. The paper considers particular interestingness measures, called confirmation measures also known as bayesian confirmation measures, used for the evaluation of if evidence, then hypothesis rules. Probability based objective interestingness measures. They are helped to classify each pattern as either interesting or uninteresting. Moal and the multiontology interestingness metrics presented in this paper have been discussed and evaluated using the go subontologies. The objective interestingness measures depend only on raw data.
These measures find patterns interesting if they are unexpected contradicting users belief or offer strategic information on which user can act. In section 3, we present a survey of seventeen interestingness measures that have been successfully employed in data mining applications. Survey of clustering data mining techniques pavel berkhin accrue software, inc. It is a data mining technique and a cluster is defined as a. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. Such facts are obvious to a domain expert since they represent common place knowledge. Association rule mining arm has been the area of interest for many researchers for a long time and continues to be the same. Exploratory data mining has as its aim to assist a user in improving their. The 2005 survey paper on interestingness measures for knowledge. Rule interestingness has become an active area of study in the fields of data mining dm and knowledge discovery in databases kdd in the last years. Some common types of patterns found in databases are clusters, itemsets, trends, and outliers.
Can confirmation measures reflect statistically sound. Highlights propose a combination between frequent itemsets lattice and hash table for mining association rules with interestingness measures. Introduction the analysis of relationships among variables is a funda mental task at the heart of many data mining problems. Today, the term interestingness and interestingness measure im is still in use in certain areas of exploratory data mining edm, in particular in the context of frequent pattern mining and notably frequent itemset and association rule mining see 5 for an excellent survey, and for a survey of noveltybased ims.
Interestingness measures for association rules within. Lattice is used to get support of itemset in the left hand side of a rule and hash tables are used to get support of itemset in the right hand side. Interestingness of association rules in data mining indian academy. Student, department of computer engineering, punjabi university. In this paper, we define group association rules and we study interestingness measures for them. Data mining keywords interestingness measure, contingency tables, associations 1. Data mining is a process to find out interesting patterns, correlations and information. The book summarizes recent developments and presents original research on this topic. We then provide a detailed survey of one important approach, namely interestingness measure, and discuss its relevance in ecommerce applica. Support and confidence are the defacto interestingness measures used for discovering relevant association rules.
Text mining preprocessing text, feature generation, feature selection, rapidminer text extension. Subjective interestingness measures are based on user belief in the data. A study on interestingness measures for associative. Apr 03, 2011 i have been unable to find a pdf version online, but the reference is r. Algorithm process data mining based on decision tree decision tree learning, used in statistics, data mining and. Ranking the interestingness of summaries from data mining. Today, the term interestingness and interestingness measure im is still in use in certain areas of exploratory data mining edm, in particular in the context of frequent pattern mining and notably frequent itemset and association rule miningsee 5 for an excellent survey, and for a survey of noveltybased ims. Permission to make digital or hard copies of all or part of this work for. Subjective interestingness in exploratory data mining. This paper is a survey that focuses on the discovery of itemsets in databases, a popular data mining task for analyzing symbolic data.
A survey of interestingness measures for knowledge. Association rules mining is an important topic in the domain of data mining and knowledge discovering. Section three describes in a general way the motivation, goals and problems of developing interestingness measures. There are clear overlaps between statistics and data mining, glymour and hand provide some insights. In this report, we provide a general overview of the more successful and widely known data mining techniques and algorithms, and survey seventeen interestingness measures from the literature. A novel method of interestingness measures for association. Interestingness of association rules in data mining 293 are so large that manual inspection and analysis is impractical if not impossible. We study data mining where the data mining task is description by summarization, the representation language is generalized relations, the evaluation criteria are based on heuristic measures of interestingness, and the method for searching is the multiattribute generalization algorithm 12 for domain generalization graphs. These are discussed in the specific context of association rules by geng and hamilton, who outline methods of choosing suitable interestingness measures.
Quality issues, measures of interestingness and evaluation. This volume presents the state of the art concerning quality and interestingness measures for data mining. Ranking the interestingness of summaries from data mining systems. Each data mining algorithm can be decomposed into four components. Most researchers divide interestingness measures into objective and subjective measures 11, 1520. To automatically evaluate which patterns are interesting and which one are not, interestingness measures are used by itemset mining algorithms. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual. Tech scholar, department of computer science and applications. The researchers point of view when designing objective interestingness measures left, where he coincides with the practitioner and subjective interestingness measures right. As a whole, qimie intend to be a forum for a communitywide discussion of these issues and to contribute to a deep crossfertilization. Interestingness measures for data mining acm digital library.
Which gives overview of data mining is used to extract meaningful information and to develop significant relationships among variables stored in. Quality issues, measures of interestingness and evaluation of. Data mining is the analysis step of the kddknowledge discovery and data mining process. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. A survey of interestingness measures for knowledge discovery 3 useful, the organisation should be able to act upon these patterns to become more pro. The study of association rules within groups of individuals in a database is interesting to define their characteristics and their behavior. The third quality issues, measures of interestingness and evaluation of data mining models workshop qimie will focus on these questions and should be of great interest for a large panel of data miners. A survey 23 in a transaction dataset, the weight on an attribute could represent the price of a com modity, and the weight on an attributevalue pair could represent the quantity of the commodity in a transaction.
In section 2, we present a general overview of classical data mining techniques and algorithms. Kdd notes 8 probability based objective interestingness. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. In this paper, using an electronic medical record emr dataset of diagnoses and medications from over three million patient visits to the university of kentucky medical center and affiliated clinics, we conduct a thorough evaluation of dozens of interestingness measures proposed in data mining literature, including some new composite measures. Hamilton, interestingness measures for data mining. Interestingness measures for association rules within groups. A total of 21 interestingness measures are considered by tan et al. In addition, most of these mined patterns represent strong domain facts. Today, the term interestingness and interestingness measure im is still in. Pdf mcgarry, k a survey of interestingness measures.
Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. Yu, fellow, ieee abstractthe main purpose of data mining and analytics is to. Many studies have been conducted concerning the formalization of rule interestingness, and concerning human substitutive evaluation of rules using formalized interestingness measures. Interestingness measures play an important role in data mining, regardless of the kind of. In itemset mining, the original measure is the support. Interestingness of association rules in data mining. Some papers have presented several interestingness measure methods. It is simply how many times a group of items occurs in a transaction database. A survey of interestingness measures for knowledge discovery. A complete survey on application of frequent pattern. Subjective interestingness in exploratory data mining springerlink. View notes kdd notes 8 from cs 831 at university of regina. A survey of text classification and clustering issues in.
In this report, we provide a general overview of the more successful and widely known data mining techniques and algorithms, and survey seventeen interestingness measures from the. A brief overview on data mining survey hemlata sahu, shalini shrma, seema gondhalakar abstract this paper provides an introduction to the basic concept of data mining. Tech scholar, department of computer science and applications, kurukshetra university, kurukshetra abstract. However, our mining and interestingness measures can be easily generalized to suit applications involving other biomedical ontologies structured as directed acyclic graphs. These measures are used to select and rank patterns according to the interest of the user. In this article, we survey measures of interestingness for data mining.
Josephs college of arts and science autonomous cuddalore abstract. A novel method of interestingness measures for association rules. It is a wellknown fact that the data mining process can generate many hundreds and often thousands of patterns from data. Interestingness measures play an important role in data mining. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. Introduction one of the many pillars of data mining is association rule mining, which is the problem where given a database of items and transactions that grouped different items together, the goal is. Association rule mining, objective interestingness measures, data mining, clustering, information retrieval. Hamilton, evaluation of interestingness measures for ranking discovered knowledge, in proceedings of the 5 th asiapacific conference on knowledge discovery and data mining, d. This paper is a survey that focuses on the discovery of itemsets in databases, a popular. A survey 7 the predictive accuracy of the ruleset on the testing data is 0. However, our mining and interestingness measures can be easily generalized to suit applications involving other biomedical. Objective interestingness measures play a vital role in association rule mining of a largescaled database because they are used for extracting, filtering, and ranking the patterns. Tuzhilin, ieee transactions on knowledge and data eng. A complete survey on application of frequent pattern mining.
As a whole, qimie15 intend to be a forum for a communitywide discussion of these issues and to contribute to a deep crossfertilization. Interestingness measures of association rules are support and confidence. Pdf knowledge discovery and interestingness measures. Moreover, sequential pattern mining can also be applied to time series e. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual worth. Comprehensible, the new patterns should be understandable to the users and add to their knowledge. These measures are intended for selecting and ranking patterns according to their potential interest to the user. A survey of text classification and clustering issues in data mining m.
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