| Title: |
Mining for Contiguous Frequent Itemsets in Transaction Databases |
| Author(s): |
C. Berberidis, G. Tzanis, I. Vlahavas.
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| Availability: |
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| Keywords: |
data mining, market basket analysis, frequent itemset mining, association rule.
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| Appeared in: |
IEEE 3rd International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2005), IEEE, pp. 679-685, Sofia, Bulgaria, 2005.
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| Abstract: |
Mining a transaction database for association rules is a particularly popular data mining task, which involves the search for frequent co-occurrences among items. One of the problems often encountered is the large number of weak rules extracted. Item taxonomies, when available, can be used to reduce them to a more usable volume. In this paper we introduce a new data mining paradigm, which involves the discovery of contiguous frequent itemsets. We formulate the problem of mining contiguous frequent itemsets in a transaction database and we present a level-wise algorithm for finding these itemsets. Contiguous frequent itemsets may contain important knowledge about the dataset, that can not be exposed by the use of classic association rule mining approaches. This knowledge may well include serious hints for the generation of a taxonomy for all or part of the items. |
| See also : |
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This paper has been cited by the following:
| 1 |
Waleed A. Aljandal, Itemset size-sensitive interestingness measures for association rule mining and link prediction. PhD Dissertation, Department of Computing and Information Sciences, College of Engineering, Kansas State University, 2008. |
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