Subjective interestingness measures are based on user belief in the data. A survey of text classification and clustering issues in data mining m. In this paper, we define group association rules and we study interestingness measures for them. Exploratory data mining has as its aim to assist a user in improving their. Pdf it is a wellknown fact that the data mining process can generate many hundreds and often thousands of patterns from data. Today, the term interestingness and interestingness measure im is still in. A complete survey on application of frequent pattern. Interestingness measures for association rules within. Tech scholar, department of computer science and applications.
They are helped to classify each pattern as either interesting or uninteresting. Hamilton, evaluation of interestingness measures for ranking discovered knowledge, in proceedings of the 5 th asiapacific conference on knowledge discovery and data mining, d. Kdd notes 8 probability based objective interestingness. Association rules mining is an important topic in the domain of data mining and knowledge discovering. Support and confidence are the defacto interestingness measures used for discovering relevant association rules. This volume presents the state of the art concerning quality and interestingness measures for data mining. The definition of quality of association rules is a well studied topic in statistics and data mining. A survey of interestingness measures for knowledge. Subjective interestingness in exploratory data mining springerlink. Selecting the right interestingness measure for association. 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. I have been unable to find a pdf version online, but the reference is r. These are discussed in the specific context of association rules by geng and hamilton, who outline methods of choosing suitable interestingness measures.
Some common types of patterns found in databases are clusters, itemsets, trends, and outliers. In addition, most of these mined patterns represent strong domain facts. 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. 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. The objective interestingness measures depend only on raw data. Quality issues, measures of interestingness and evaluation of. Introduction the analysis of relationships among variables is a funda mental task at the heart of many data mining problems.
A survey 7 the predictive accuracy of the ruleset on the testing data is 0. Permission to make digital or hard copies of all or part of this work for. A novel method of interestingness measures for association rules. In this article, we survey measures of interestingness for data mining. 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. A survey of interestingness measures for knowledge discovery. Data mining is a process to find out interesting patterns, correlations and information. Can confirmation measures reflect statistically sound. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. 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. Good measures also allow the time and space costs of the mining process to be reduced. It is simply how many times a group of items occurs in a transaction database. In this paper, we aim to formalize the data mining process.
Section three describes in a general way the motivation, goals and problems of developing interestingness measures. The book summarizes recent developments and presents original research on this topic. However, our mining and interestingness measures can be easily generalized to suit applications involving other biomedical. 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. This paper is a survey that focuses on the discovery of itemsets in databases, a popular data mining task for analyzing symbolic data. Yu, fellow, ieee abstractthe main purpose of data mining and analytics is to. The study of association rules within groups of individuals in a database is interesting to define their characteristics and their behavior. These measures find patterns interesting if they are unexpected contradicting users belief or offer strategic information on which user can act. Algorithm process data mining based on decision tree decision tree learning, used in statistics, data mining and. Comprehensible, the new patterns should be understandable to the users and add to their knowledge. Pdf mcgarry, k a survey of interestingness measures. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual worth.
Interestingness of association rules in data mining. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual. Interesting patterns and constraints based data mining interesting patterns are knowledge based 8 and are easy to understand. Highlights propose a combination between frequent itemsets lattice and hash table for mining association rules with interestingness measures. Association rule mining, objective interestingness measures, data mining, clustering, information retrieval. Quality issues, measures of interestingness and evaluation. Association rule mining arm has been the area of interest for many researchers for a long time and continues to be the same. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. These evaluation measures can be used to rank groups of individuals and also rules within each group. View notes kdd notes 8 from cs 831 at university of regina. Interestingness measures for data mining acm digital library. Moal and the multiontology interestingness metrics presented in this paper have been discussed and evaluated using the go subontologies.
A study on interestingness measures for associative. 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. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. Interestingness of association rules in data mining 293 are so large that manual inspection and analysis is impractical if not impossible. It is a wellknown fact that the data mining process can generate many hundreds and often thousands of patterns from data. Each data mining algorithm can be decomposed into four components. To automatically evaluate which patterns are interesting and which one are not, interestingness measures are used by itemset mining algorithms. A very important aspect of data mining research is the determination of how interesting a pattern is.
Interestingness measures for association rules within groups. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. In section 2, we present a general overview of classical data mining techniques and algorithms. Probability based objective interestingness measures reference. Some papers have presented several interestingness measure methods. Which gives overview of data mining is used to extract meaningful information and to develop significant relationships among variables stored in. There are clear overlaps between statistics and data mining, glymour and hand provide some insights. Part of their analysis includes identifying interestingness measures that satisfied the three properties of piatetskyshapiro as well as the five additional properties. 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. Information about other references can be found in the interestingness measures. Data mining and concepts and techniques, by jiawei han and micheline kamber. The 2005 survey paper on interestingness measures for knowledge.
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. Most researchers divide interestingness measures into objective and subjective measures 11, 1520. Josephs college of arts and science autonomous cuddalore abstract. Interestingness of association rules in data mining indian academy. 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. 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. A novel method of interestingness measures for association. 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. As a whole, qimie intend to be a forum for a communitywide discussion of these issues and to contribute to a deep crossfertilization. On interestingness measures for mining statistically. Apr 03, 2011 i have been unable to find a pdf version online, but the reference is r.
Pdf knowledge discovery and interestingness measures. A survey of interestingness measures for knowledge discovery 3 useful, the organisation should be able to act upon these patterns to become more pro. 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. Survey of clustering data mining techniques pavel berkhin accrue software, inc. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative. Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. Tech scholar, department of computer science and applications, kurukshetra university, kurukshetra abstract. The researchers point of view when designing objective interestingness measures left, where he coincides with the practitioner and subjective interestingness measures right. Interestingness measures play an important role in data mining, regardless of the kind of. 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. The agreement of such measures with a statistically sound significant dependency between the evidence and the hypothesis in data is. These measures are used to select and rank patterns according to the interest of the user.
Quality measures in data mining fabrice guillet springer. In itemset mining, the original measure is the support. Interestingness measures play an important role in data mining. Measures of patterns interestingness whether subjective or objective. These measures are intended for selecting and ranking patterns according to their potential interest to the user.
Section four concentrates on objective interestingness measures. As a whole, qimie15 intend to be a forum for a communitywide discussion of these issues and to contribute to a deep crossfertilization. We then provide a detailed survey of one important approach, namely interestingness measure, and discuss its relevance in ecommerce applica. 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.
Standardizing interestingness measures for association. The task for the data miner then becomes one of determining the most useful patterns from those that are trivial or are already well known to the organization. A survey of utilityoriented pattern mining wensheng gan, jerry chunwei lin, senior member, ieee, philippe fournierviger, hanchieh chao, vincent s. Text mining preprocessing text, feature generation, feature selection, rapidminer text extension. However, our mining and interestingness measures can be easily generalized to suit applications involving other biomedical ontologies structured as directed acyclic graphs. This paper is a survey that focuses on the discovery of itemsets in databases, a popular. Association strives to discover patterns in data which are based upon relationships between items in the same transaction. Data mining is the analysis step of the kddknowledge discovery and data mining process. Moreover, sequential pattern mining can also be applied to time series e. Hamilton, interestingness measures for data mining. In section 3, we present a survey of seventeen interestingness measures that have been successfully employed in data mining applications. 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.
Clustering is a division of data into groups of similar objects. Tuzhilin, ieee transactions on knowledge and data eng. A complete survey on application of frequent pattern mining. Such facts are obvious to a domain expert since they represent common place knowledge. Consider any association rule mining algorithm, apriori or fp tree. A survey of text classification and clustering issues in. Data mining keywords interestingness measure, contingency tables, associations 1.
756 219 220 558 1607 342 1014 1407 1647 1431 253 1576 621 526 1135 584 773 1166 980 967 157 790 323 101 897 526 609 1112 1160 331 122 1350 1244 164 1630 114 1203 528 1326 1106 1410 652 65 218 643 1205 1407