Data mining is an interdisciplinary research area spanning severals disciplines such as database systems, machine learning, intelligent information systems, statistics, and expert systems. Data mining has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Many aspects of data mining have been investigated in several related fields. A unique but important aspect of the problem lies in the significance of needs to extend these studies to include the nature of the contents of the real-world databases. In this chapter, we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. Since a major focus of this book is on rough sets and its applications to database mining, one full section is devoted to summari...
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4713
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
– Pearl
- 1988
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3018
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Pattern Classification and Scene Analysis
– Duda, Hart
- 1973
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2537
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Induction of decision trees
– Quinlan
- 1986
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1213
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An Introduction to the Bootstrap
– Efron, Robert
- 1993
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483
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Generalization as search
– Mitchell
- 1982
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326
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Restructuring lattice theory: an approach based on hierarchies of concepts
– Wille
- 1982
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305
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Computer Systems that Learn
– Weiss, Kulikowski
- 1991
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259
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Knowledge Discovery in Databases: An Overview, introductory chapter of Knowledge Discovery
– Frawley, Piatetsky-Shapiro, et al.
- 1991
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215
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Database Mining: A Performance Perspective
– Agrawal, Imielinski, et al.
- 1993
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169
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Learning with many irrelevant features
– Almuallim, Dietterich
- 1991
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161
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The management of probabilistic data
– Barbará, Garcia-Molina, et al.
- 1992
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157
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Projection pursuit
– Huber
- 1985
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143
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Subset Selection in Regression
– Miller
- 1990
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140
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A Branch and Bound Algorithm for Feature Subset Selection
– Narendra, Fukunaga
- 1997
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134
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An empirical comparison of selection measures for decision-tree induction
– Mingers
- 1989
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128
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Knowledge discovery in databases: An attribute-oriented approach
– Han, Cai, et al.
- 1992
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123
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Concept Learning and the Problem of Small Disjuncts
– Holte, Acker, et al.
- 1989
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101
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Database systems: Achievements and opportunities
– Silberschatz, Stonebraker, et al.
- 1991
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100
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An interval classifier for database mining applications
– Agrawal, Ghosh, et al.
- 1992
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95
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Unknown attribute values in induction
– Quinlan
- 1989
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94
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The e ect of noise on concept learning
– Quinlan
- 1986
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83
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Systems for knowledge discovery in databases
– Matheus, Chan, et al.
- 1993
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82
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The Attribute Selection Problem in Decision Tree Generation
– Fayyad, Irani
- 1992
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69
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Classification Algorithms
– James
- 1985
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41
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Knowledge Discovery from Telecommunication Network Alarm Databases
– Hotanen, Klemettinen, et al.
- 1996
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38
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Class-dependent discretization for inductive learning from continuous and mixed-mode data
– Ching, Wong, et al.
- 1995
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38
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Compression, Significance and Accuracy
– Muggleton, Srinivasan, et al.
- 1992
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37
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The Discovery, Analysis, and Representation of Data Dependencies in Databases
– ZIARKO
- 1991
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37
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Feature selection using rough sets theory
– Modrzejewski
- 1993
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34
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Rough Classification
– Pawlak
- 1984
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33
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A statistical technique for extracting classificatory knowledge from databases
– CHAN, WONG
- 1991
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27
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Effects of sample size in classifier design
– Fukunaga, Hayes
- 1989
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26
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A decision theoretic framework for approximating concepts
– Yao, Wong
- 1992
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25
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A support system for interpreting statistical data
– Hoschka, Klosgen
- 1991
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23
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A method for attribute selection in inductive learning systems
– Baim
- 1988
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21
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Learning useful rules from inconclusive data
– Uthurusamy, Fayyad, et al.
- 1991
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21
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Learning with nested generalized exemplars
– Salzberg
- 1990
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20
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An extended Relational Database Model for Uncertain and Imprecise Information
– Lee
- 1992
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18
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Accelerated quantification of Bayesian networks with incomplete data
– Thiesson
- 1995
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16
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Knowledge discovery workbench for exploring business databases
– Piatetsky-Shapiro, Matheus
- 1992
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16
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Comparison of the probabilistic approximate classification and the fuzzy set model, Fuzzy Sets and Systems
– Wong, Ziarko
- 1987
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15
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A fuzzy model for relational databases
– Buckles, Petry
- 1982
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13
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Multi-interval discretization of continuous attributes as preprocessing for classification learning
– Fayyad, Irani
- 1993
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12
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SPOTLIGHT: A Data Explanation System
– Anand, Kahn
- 1992
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11
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Comparison of rough-set and statistical methods in inductive learning
– Wong, Ziarko, et al.
- 1986
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11
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Rough classification of patients after highly selective vagotomy for duodenal ulcer
– Pawlak, Slowinski, et al.
- 1986
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11
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Dynamic programming as applied to feature subset selection in a pattern recognition system
– Chang
- 1973
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11
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Rough classification with valued closeness relation
– Slowinski, Stefanowiski
- 1995
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11
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KDD-R: A comprehensive system for knowledge discovery in databases using rough sets
– Ziarko, Shan
- 1994
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11
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The rule induction system LERS - a version for personal computers
– Chmielewski, Grzymala-Busse, et al.
- 1993
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