A Framework to Mine High-Level Emerging Patterns by Attribute-Oriented Induction

This paper presents a framework to mine summary emerging patterns in contrast to the familiar low-level patterns. Generally, growth rate based on low-level data and simple supports are used to measure emerging patterns (EP) from one dataset to another. This consequently leads to numerous EPs because of the large numbers of items. We propose an approach that uses high-level data: high-level data captures the data semantics of a collection of attributes values by using taxonomies, and always has larger support than low-level data. We apply a well known algorithm, attribute-oriented induction (AOI), that generalises attributes using taxonomies and investigate properties of the rule sets obtained by generalisation algorithms.

[1]  Michelangelo Ceci,et al.  Emerging Pattern Based Classification in Relational Data Mining , 2008, DEXA.

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

[3]  José Francisco Martínez Trinidad,et al.  Fuzzy emerging patterns for classifying hard domains , 2011, Knowledge and Information Systems.

[4]  Carlos Bento,et al.  A Metric for Selection of the Most Promising Rules , 1998, PKDD.

[5]  Rajjan Shinghal,et al.  Evaluating the Interestingness of Characteristic Rules , 1996, KDD.

[6]  Kotagiri Ramamohanarao,et al.  Fast discovery and the generalization of strong jumping emerging patterns for building compact and accurate classifiers , 2006, IEEE Transactions on Knowledge and Data Engineering.

[7]  D. Madigan,et al.  Proceedings : KDD-99 : the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 15-18, 1999, San Diego, California, USA , 1999 .

[8]  Abdul Sattar,et al.  AI 2006: Advances in Artificial Intelligence, 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006, Proceedings , 2006, Australian Conference on Artificial Intelligence.

[9]  James Bailey,et al.  Mining Generalised Emerging Patterns , 2006, Australian Conference on Artificial Intelligence.

[10]  Howard J. Hamilton,et al.  Knowledge discovery and measures of interest , 2001 .

[11]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[12]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[13]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.

[14]  Kotagiri Ramamohanarao,et al.  Patterns Based Classifiers , 2007, World Wide Web.

[15]  Ray-I Chang,et al.  From data to global generalized knowledge , 2012, Decis. Support Syst..

[16]  Jiawei Han,et al.  Attribute-Oriented Induction in Relational Databases , 1991, Knowledge Discovery in Databases.

[17]  Jinyan Li,et al.  Mining border descriptions of emerging patterns from dataset pairs , 2005, Knowledge and Information Systems.

[18]  Wenfei Fan,et al.  Keys with Upward Wildcards for XML , 2001, DEXA.