Understanding the crucial differences between classification and discovery of association rules: a position paper

The goal of this position paper is to contribute to a clear understanding of the profound differences between the association-rule discovery and the classification tasks. We argue that the classification task can be considered an ill-defined, nondeterministic task, which is unavoidable given the fact that it involves prediction; while the standard association task can be considered a well-defined, deterministic, relatively simple task, which does not involve prediction in the same sense as the classification task does.

[1]  David W. Aha,et al.  Simplifying decision trees: A survey , 1997, The Knowledge Engineering Review.

[2]  W. Spears,et al.  For Every Generalization Action, Is There Really an Equal and Opposite Reaction? , 1995, ICML.

[3]  Hussein H. Aly,et al.  Mining association rules , 2001, CATA.

[4]  Tim Oates,et al.  Large Datasets Lead to Overly Complex Models: An Explanation and a Solution , 1998, KDD.

[5]  Ehud Gudes,et al.  FlexiMine - A Flexible Platform for KDD Research and Application Construction , 1998, KDD.

[6]  Cullen Schaffer,et al.  A Conservation Law for Generalization Performance , 1994, ICML.

[7]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[8]  Fabrice Guillet,et al.  Improving the Discovery of Association Rules with Intensity of Implication , 1998, PKDD.

[9]  Max A. Bramer,et al.  Induction of classification rules from examples: a critical review , 1996 .

[10]  David D. Jensen,et al.  Adjusting for Multiple Comparisons in Decision Tree Pruning , 1997, KDD.

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

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

[13]  Roberto J. Bayardo Brute-Force Mining of High-Confidence Classification Rules , 1997, KDD.

[14]  Xindong Wu,et al.  Research and Development in Knowledge Discovery and Data Mining , 1998, Lecture Notes in Computer Science.

[15]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[16]  Wynne Hsu,et al.  Pruning and summarizing the discovered associations , 1999, KDD '99.

[17]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

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

[19]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[20]  D. Wolpert On Overfitting Avoidance as Bias , 1993 .

[21]  Sandy Lovie How the mind works , 1980, Nature.

[22]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[23]  S. Pinker How the Mind Works , 1999, Philosophy after Darwin.

[24]  Heikki Mannila,et al.  Association Rule Selection in a Data Mining Environment , 1999, PKDD.

[25]  Padhraic Smyth,et al.  Rule Induction Using Information Theory , 1991, Knowledge Discovery in Databases.

[26]  Jinyan Li,et al.  Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness , 1998, PAKDD.