ART: A Hybrid Classification Model

This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for ‘Association Rule Tree’, builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized “Separate and Conquer” algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

[1]  Yiming Ma,et al.  Improving an Association Rule Based Classifier , 2000, PKDD.

[2]  Philip K. Chan,et al.  Inductive Learning with BCT , 1989, ML.

[3]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[4]  Christian Hidber,et al.  Association Rule Mining , 2017 .

[5]  Zijian Zheng,et al.  Constructing X-of-N Attributes for Decision Tree Learning , 2000, Machine Learning.

[6]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[7]  Wynne Hsu,et al.  Intuitive Representation of Decision Trees Using General Rules and Exceptions , 2000, AAAI/IAAI.

[8]  Ke Wang,et al.  Building Hierarchical Classifiers Using Class Proximity , 1999, VLDB.

[9]  Philip S. Yu,et al.  Using a Hash-Based Method with Transaction Trimming for Mining Association Rules , 1997, IEEE Trans. Knowl. Data Eng..

[10]  Kyuseok Shim,et al.  PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning , 1998, Data Mining and Knowledge Discovery.

[11]  Renée J. Miller,et al.  Association rules over interval data , 1997, SIGMOD '97.

[12]  Kamal Ali,et al.  Partial Classification Using Association Rules , 1997, KDD.

[13]  IV JohnF.Elder,et al.  Heuristic Search for Model Structure: the Benefits of Restraining Greed , 1995, AISTATS.

[14]  Pedro M. Domingos.  Linear-Time Rule Induction , 1996, KDD.

[15]  Oren Etzioni,et al.  Learning Decision Lists Using Homogeneous Rules , 1994, AAAI.

[16]  Nicolás Marín,et al.  TBAR: An efficient method for association rule mining in relational databases , 2001, Data Knowl. Eng..

[17]  Ke Wang,et al.  Growing decision trees on support-less association rules , 2000, KDD '00.

[18]  Johannes Gehrke,et al.  Classification and regression: money *can* grow on trees , 1999, KDD '99.

[19]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[20]  Rakesh Agrawal,et al.  SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.

[21]  Dimitris Meretakis,et al.  Extending naïve Bayes classifiers using long itemsets , 1999, KDD '99.

[22]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[23]  Jinyan Li,et al.  CAEP: Classification by Aggregating Emerging Patterns , 1999, Discovery Science.

[24]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[25]  Johannes Gehrke,et al.  BOAT—optimistic decision tree construction , 1999, SIGMOD '99.

[26]  William G. Griswold,et al.  Getting started with ASPECTJ , 2001, CACM.

[27]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[28]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[29]  Philip S. Yu,et al.  A new framework for itemset generation , 1998, PODS '98.

[30]  Jorma Rissanen,et al.  SLIQ: A Fast Scalable Classifier for Data Mining , 1996, EDBT.

[31]  J. Ross Quinlan,et al.  Learning decision tree classifiers , 1996, CSUR.

[32]  D. Haussler,et al.  Boolean Feature Discovery in Empirical Learning , 1990, Machine Learning.

[33]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[34]  M.A.W. Houtsma,et al.  Set-Oriented Mining for Association Rules , 1993, ICDE 1993.

[35]  Pedro M. Domingos.  The Role of Occam's Razor in Knowledge Discovery , 1999, Data Mining and Knowledge Discovery.

[36]  Alex Alves Freitas,et al.  Understanding the crucial differences between classification and discovery of association rules: a position paper , 2000, SKDD.

[37]  Wynne Hsu,et al.  Multi-level organization and summarization of the discovered rules , 2000, KDD '00.

[38]  Ronald L. Rivest,et al.  Learning decision lists , 2004, Machine Learning.

[39]  Filippo Neri,et al.  Search-Intensive Concept Induction , 1995, Evolutionary Computation.

[40]  Johannes Fürnkranz,et al.  Incremental Reduced Error Pruning , 1994, ICML.

[41]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[42]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[43]  JOHANNES GEHRKE,et al.  RainForest—A Framework for Fast Decision Tree Construction of Large Datasets , 1998, Data Mining and Knowledge Discovery.

[44]  Jia Liang Han,et al.  Background for association rules and cost estimate of selected mining algorithms , 1996, CIKM '96.

[45]  Philip S. Yu,et al.  Online algorithms for finding profile association rules , 1998, CIKM '98.

[46]  Tharam S. Dillon,et al.  Automated knowledge acquisition , 1994, Prentice Hall International series in computer science and engineering.

[47]  Philip S. Yu,et al.  Mining Large Itemsets for Association Rules , 1998, IEEE Data Eng. Bull..

[48]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[49]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[50]  Yehuda Lindell,et al.  A Statistical Theory for Quantitative Association Rules , 1999, KDD '99.

[51]  Pedro M. Domingos.  Occam's Two Razors: The Sharp and the Blunt , 1998, KDD.

[52]  W. Loh,et al.  SPLIT SELECTION METHODS FOR CLASSIFICATION TREES , 1997 .

[53]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[54]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[55]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[56]  Vipin Kumar,et al.  Mining needle in a haystack: classifying rare classes via two-phase rule induction , 2001, SIGMOD '01.

[57]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.