A Framework for Pattern-Based Global Models

Discovering global models on a dataset (e.g., classifiers, clusterings, summaries) has attracted a lot of attention and many approaches can be found in the literature. However no framework has been proposed yet for describing and comparing these approaches in a uniform manner. In this paper we propose such a framework for pattern-based modeling approaches, i.e., approaches that use local patterns to construct a global model. This framework includes a generic algorithm (IGMA) for constructing a global model. We show that the framework allows to describe in an as declarative as possible way various different global model construction methods.

[1]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[2]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[3]  Jian Tang,et al.  Mining N-most Interesting Itemsets , 2000, ISMIS.

[4]  Heikki Mannila,et al.  The Pattern Ordering Problem , 2003, PKDD.

[5]  Albrecht Zimmermann,et al.  The Chosen Few: On Identifying Valuable Patterns , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[6]  JOHANNES FÜRNKRANZ,et al.  Separate-and-Conquer Rule Learning , 1999, Artificial Intelligence Review.

[7]  Luc De Raedt,et al.  IQL: A Proposal for an Inductive Query Language , 2006, KDID.

[8]  Luc De Raedt,et al.  Constraint-Based Pattern Set Mining , 2007, SDM.

[9]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[10]  Johannes Fürnkranz,et al.  From Local Patterns to Global Models: The LeGo Approach to Data Mining , 2008 .

[11]  Arno J. Knobbe,et al.  Pattern Teams , 2006, PKDD.

[12]  Johannes Fürnkranz,et al.  Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings , 2006, PKDD.

[13]  Arnaud Giacometti,et al.  A Generic Framework for Rule-Based Classification , 2008 .

[14]  Nicolas Durand,et al.  ECCLAT: a New Approach of Clusters Discovery in Categorical Data , 2003 .

[15]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

[16]  Francesco Bonchi,et al.  Knowledge Discovery in Inductive Databases, 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers , 2006, KDID.

[17]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[18]  Shusaku Tsumoto,et al.  Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, May 25-28, 2005, Proceedings , 2005, ISMIS.

[19]  Hendrik Blockeel,et al.  Knowledge Discovery in Databases: PKDD 2003 , 2003, Lecture Notes in Computer Science.

[20]  Vipin Kumar,et al.  Clustering Based On Association Rule Hypergraphs , 1997, DMKD.