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Closed multidimensional sequential pattern mining

Closed multidimensional sequential pattern mining We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed itemset pattern mining. This integration is performed in two approaches: mining closed itemset patterns followed by mining closed sequential patterns and mining closed sequential patterns followed by mining closed itemset patterns. For the new method, it is shown that (1) the number of complete closed multidimensional sequential patterns is not larger than the number of complete multidimensional sequential patterns; (2) the set of complete closed multidimensional sequential patterns covers the complete resulting set of multidimensional sequential patterns. In addition, mining using closed itemset pattern mining on multidimensional information would mine only multidimensional information associated with mined closed sequential patterns, and mining using closed sequential pattern mining on sequences would mine only sequences associated with mined closed itemset patterns. Moreover, we experimentally investigated using these two integration approaches to determine factors affecting the number of candidate patterns. The experiment results show that the first approach gives less number of patterns than the second on the datasets with few dimensions. For the data sets with many dimensions, the results are opposite. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge Management Studies Inderscience Publishers

Closed multidimensional sequential pattern mining

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1743-8268
eISSN
1743-8276
DOI
10.1504/IJKMS.2008.019752
Publisher site
See Article on Publisher Site

Abstract

We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed itemset pattern mining. This integration is performed in two approaches: mining closed itemset patterns followed by mining closed sequential patterns and mining closed sequential patterns followed by mining closed itemset patterns. For the new method, it is shown that (1) the number of complete closed multidimensional sequential patterns is not larger than the number of complete multidimensional sequential patterns; (2) the set of complete closed multidimensional sequential patterns covers the complete resulting set of multidimensional sequential patterns. In addition, mining using closed itemset pattern mining on multidimensional information would mine only multidimensional information associated with mined closed sequential patterns, and mining using closed sequential pattern mining on sequences would mine only sequences associated with mined closed itemset patterns. Moreover, we experimentally investigated using these two integration approaches to determine factors affecting the number of candidate patterns. The experiment results show that the first approach gives less number of patterns than the second on the datasets with few dimensions. For the data sets with many dimensions, the results are opposite.

Journal

International Journal of Knowledge Management StudiesInderscience Publishers

Published: Jan 1, 2008

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