Emerging Patterns and Classification

In this work, we review an important kind of knowledge pattern, emerging patterns (EPs). Emerging patterns are associated with two data sets, and can be used to describe significant changes between the two data sets. To discover all EPs embedded in high-dimension and large-volume databases is a challenging problem due to the number of candidates. We describe a special type of EP, called jumping emerging patterns (JEPs) and review some properties of JEP spaces (the spaces of jumping emerging patterns). We describe efficient border-based algorithms to derive the boundary elements of JEP spaces. Moreover, we describe a new classifier, called DeEPs, which makes use of the discriminating power of emerging patterns. The experimental results show that the accuracy of DeEPs is much better than that of k-nearest neighbor and that of C5.0.

[1]  Kotagiri Ramamohanarao,et al.  The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms , 2000, ICML.

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

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

[4]  Tom M. Mitchell,et al.  Version Spaces: A Candidate Elimination Approach to Rule Learning , 1977, IJCAI.

[5]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[6]  Kotagiri Ramamohanarao,et al.  Instance-Based Classification by Emerging Patterns , 2000, PKDD.

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

[8]  Jinyan Li,et al.  Eecient Mining of Emerging Patterns: Discovering Trends and Diierences , 1999 .

[9]  Devika Subramanian,et al.  The Common Order-Theoretic Structure of Version Spaces and ATMSs , 1991, Artif. Intell..

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

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

[12]  Roberto J. Bayardo,et al.  Efficiently mining long patterns from databases , 1998, SIGMOD '98.

[13]  Kotagiri Ramamohanarao,et al.  Making Use of the Most Expressive Jumping Emerging Patterns for Classification , 2000, Knowledge and Information Systems.

[14]  Kotagiri Ramamohanarao,et al.  Efficient Mining of High Confidience Association Rules without Support Thresholds , 1999, PKDD.

[15]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

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

[17]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .