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41
Interestingness measures for data mining: a survey
 ACM Computing Surveys
"... Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to ..."
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Cited by 158 (2 self)
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Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area.
On Modeling Data Mining with Granular Computing
 Proceedings of COMPSAC 2001
, 2001
"... The main objective of this paper is to advocate for formal and mathematical modeling of data mining, which unfortunately has not received much attention. A framework is proposed for rule mining based on granular computing. It is developed in the Tarski's style through the notions of a model and ..."
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Cited by 42 (19 self)
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The main objective of this paper is to advocate for formal and mathematical modeling of data mining, which unfortunately has not received much attention. A framework is proposed for rule mining based on granular computing. It is developed in the Tarski's style through the notions of a model and satisfiability. The model is a database consisting of a finite set of objects described by a finite set of attributes. Within this framework, a concept is defined as a pair consisting of the intension, an expression in a certain language over the set of attributes, and the extension, a subset of the universe, of the concept. An object satisfies the expression of a concept if the object has the properties as specified by the expression, and the object belongs to the extension of the concepts. Rules are used to describe relationships between concepts. A rule is expressed in terms of the intensions of the two concepts and is interpreted in terms of the extensions of the concepts. Two interpretations of rules are examined in detail, one is based on logical implication and the other on conditional probability.
Informationtheoretic measures for knowledge discovery and data mining.”
 In Entropy Measures, Maximum Entropy and Emerging Applications, Karmeshu, ed., Vol. 119 of Studies in Fuzziness and Soft Computing.
, 2003
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Attribute Reduction in DecisionTheoretic Rough Set Models
 INFORMATION SCIENCES, 178(17), 33563373, ELSEVIER B.V.
, 2008
"... Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also th ..."
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Cited by 29 (2 self)
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Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also the semantic measures such as decisionmonotocity, cost and risk. The classification rules can be evaluated locally for each individual rule, or globally for a set of rules. Both the two types of classification rules can be generated from, and interpreted by, a decisiontheoretic model, which is a probabilistic extension of the Pawlak rough set model. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. This paper addresses attribute reduction in decisiontheoretic rough set models regarding different classification properties, such as: decisionmonotocity, confidence, coverage, generality and cost. It is important to note that many of these properties can be truthfully reflected by a single measure γ in the Pawlak rough set model. On the other hand, they need to be considered separately in probabilistic models. A straightforward extension of the γ measure is unable to evaluate these properties. This study provides a new insight into the problem of attribute reduction.
Knowledge discovery with genetic programming for providing feedback to courseware author. User Modeling and Useradapted Interaction: The
 Journal of Personalization Research
"... Abstract. We introduce a methodology to improve Adaptive Systems for WebBased Education. This methodology uses evolutionary algorithms as a data mining method for discovering interesting relationships in students ’ usage data. Such knowledge may be very useful for teachers and course authors to sel ..."
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Cited by 25 (12 self)
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Abstract. We introduce a methodology to improve Adaptive Systems for WebBased Education. This methodology uses evolutionary algorithms as a data mining method for discovering interesting relationships in students ’ usage data. Such knowledge may be very useful for teachers and course authors to select the most appropriate modifications to improve the effectiveness of the course. We use GrammarBased Genetic Programming (GBGP) with multiobjective optimization techniques to discover prediction rules. We present a specific data mining tool that can help nonexperts in data mining carry out the complete rule discovery process, and demonstrate its utility by applying it to an adaptive Linux course that we developed. Key words. adaptive system for webbased education, data mining, evolutionary algorithms, grammarbased genetic programming, prediction rules
Probabilistic approaches to rough sets
 Expert Systems
, 2003
"... This paper reviews probabilistic approaches to rough sets in granulation, approximation, and rule induction. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic approxim ..."
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Cited by 22 (10 self)
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This paper reviews probabilistic approaches to rough sets in granulation, approximation, and rule induction. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic approximations are defined in a decisiontheoretic framework. The problem of rule induction, a major application of rough set theory, is studied in probabilistic and informationtheoretic terms. Two types of rules are analyzed, the local, low order rules, and the global, high order rules. 1
Potential Applications of Granular Computing in Knowledge Discovery and Data Mining
, 1999
"... In this paper, we argue that granular computing may have many potential applications in knowledge discovery and data mining. Three related basic operations of granular computing are examined: granulation of the universe, characterization of granules, and relationships between granules. Their connect ..."
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Cited by 21 (8 self)
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In this paper, we argue that granular computing may have many potential applications in knowledge discovery and data mining. Three related basic operations of granular computing are examined: granulation of the universe, characterization of granules, and relationships between granules. Their connections to the tasks of knowledge discovery and data mining are analyzed.
A generalized decision logic language for granular computing
 In Proceedings of the 11th IEEE International Conference on Fuzzy Systems
, 2002
"... Abstract A generalized decision logic language GDL is proposed for granular computing (GrC) in the Tarski’s style through the notions of a model and satisfiability. The model is an information table consisting of a finite set of objects described by a finite set of attributes. A concept or a granul ..."
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Cited by 19 (12 self)
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Abstract A generalized decision logic language GDL is proposed for granular computing (GrC) in the Tarski’s style through the notions of a model and satisfiability. The model is an information table consisting of a finite set of objects described by a finite set of attributes. A concept or a granule is characterized by a pair consisting of the intension of the concept, a formula of the language GDL, and the extension of the concept, a subset of the universe. We discuss the application of GDL in formal concepts and decision rules. The former deals with description and interpretation of granules, and the latter deals with the relationships between granules. I.
Granular Computing Using Information Tables
 In: Data Mining, Rough Sets and Granular Computing
, 2002
"... Abstract. A simple and more concrete granular computing model may be developed using the notion of information tables. In this framework, each object in a finite nonempty universe is described by a finite set of attributes. Based on attribute values of objects, one may decompose the universe into pa ..."
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Cited by 19 (10 self)
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Abstract. A simple and more concrete granular computing model may be developed using the notion of information tables. In this framework, each object in a finite nonempty universe is described by a finite set of attributes. Based on attribute values of objects, one may decompose the universe into parts called granules. Objects in each granule share the same or similar description in terms of their attribute values. Studies along this line have been carried out in the theories of rough sets and databases. Within the proposed model, this paper reviews the pertinent existing results and presents their generalizations and applications. 1
A Measurementtheoretic foundation for rule interestingness evaluation
 Proceedings of Workshop on Foundations and New Directions in Data Mining in the Third IEEE International Conference on Data Mining (ICDM 2003
, 2003
"... Summary. Many measures have been proposed and studied extensively in data mining for evaluating the interestingness (or usefulness) of discovered rules. They are usually defined based on structural characteristics or statistical information about the rules. The meaningfulness of each measure was int ..."
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Cited by 10 (7 self)
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Summary. Many measures have been proposed and studied extensively in data mining for evaluating the interestingness (or usefulness) of discovered rules. They are usually defined based on structural characteristics or statistical information about the rules. The meaningfulness of each measure was interpreted based either on intuitive arguments or mathematical properties. There does not exist a framework in which one is able to represent the user judgment explicitly, precisely, and formally. Since the usefulness of discovered rules must be eventually judged by users, a framework that takes user preference or judgement into consideration will be very valuable. The objective of this paper is to propose such a framework based on the notion of user preference. The results are useful in establishing a measurementtheoretic foundation of rule interestingness evaluation.