%0 Book Section %1 citeulike:1821040 %A Minato, Shin-Ichi %A Arimura, Hiroki %D 2007 %J Knowledge Discovery in Inductive Databases %K cognitive\_schemas, cultural\_schemas, data\_mining, pattern\_mining %P 152--169 %R 10.1007/978-3-540-75549-4\_10 %T Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs %U http://dx.doi.org/10.1007/978-3-540-75549-4\_10 %X Frequent pattern mining is one of the fundamental techniques for knowledge discovery and data mining. During the last decade, several efficient algorithms for frequent pattern mining have been presented, but most algorithms have focused on enumerating the patterns that satisfy the given conditions, considering the storage and indexing of the pattern results for efficient inductive analysis to be a separate issue. In this paper, we propose a fast algorithm for extracting all/maximal frequent patterns from transaction databases and simultaneously indexing a huge number of patterns using Zero-suppressed Binary Decision Diagrams (ZBDDs). Our method is comparably fast as existing state-of-the-art algorithms and not only enumerates/lists the patterns but also compactly indexes the output data in main memory. After mining, the pattern results can be analyzed efficiently by using algebraic operations. BDD-based data structures have previously been used successfully in VLSI logic design, but our method is the first practical application of BDD-based techniques in the data mining area.

@incollection{citeulike:1821040, abstract = {Frequent pattern mining is one of the fundamental techniques for knowledge discovery and data mining. During the last decade, several efficient algorithms for frequent pattern mining have been presented, but most algorithms have focused on enumerating the patterns that satisfy the given conditions, considering the storage and indexing of the pattern results for efficient inductive analysis to be a separate issue. In this paper, we propose a fast algorithm for extracting all/maximal frequent patterns from transaction databases and simultaneously indexing a huge number of patterns using Zero-suppressed Binary Decision Diagrams (ZBDDs). Our method is comparably fast as existing state-of-the-art algorithms and not only enumerates/lists the patterns but also compactly indexes the output data in main memory. After mining, the pattern results can be analyzed efficiently by using algebraic operations. BDD-based data structures have previously been used successfully in VLSI logic design, but our method is the first practical application of BDD-based techniques in the data mining area.}, added-at = {2009-12-11T23:34:46.000+0100}, author = {Minato, Shin-Ichi and Arimura, Hiroki}, biburl = {https://www.bibsonomy.org/bibtex/2dc7f68092ef3d032dc80dd9a61748df1/djsaab}, citeulike-article-id = {1821040}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-75549-4\_10}, description = {djsaab's CiteULike library 20091211}, doi = {10.1007/978-3-540-75549-4\_10}, interhash = {d0ac5ffb97fc6ca7871ffcd066444cce}, intrahash = {dc7f68092ef3d032dc80dd9a61748df1}, journal = {Knowledge Discovery in Inductive Databases}, keywords = {cognitive\_schemas, cultural\_schemas, data\_mining, pattern\_mining}, pages = {152--169}, posted-at = {2007-10-25 14:39:41}, priority = {3}, timestamp = {2009-12-11T23:35:11.000+0100}, title = {Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs}, url = {http://dx.doi.org/10.1007/978-3-540-75549-4\_10}, year = 2007 }