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CanTree: a canonical-order tree for incremental frequent-pattern mining

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Abstract

Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree-based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree in the incremental mining of frequent patterns. Moreover, the applicability of CanTrees is not confined to incremental mining; CanTrees can also be applicable to other frequent-pattern mining tasks including constrained mining and interactive mining.

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Authors and Affiliations

  1. Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada, R3T 2N2

    Carson Kai-Sang Leung, Quamrul I. Khan, Zhan Li & Tariqul Hoque

Authors
  1. Carson Kai-Sang Leung
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  2. Quamrul I. Khan
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  3. Zhan Li
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  4. Tariqul Hoque
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Correspondence to Carson Kai-Sang Leung.

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Carson K.-S. Leung received his B.Sc.(Honours), M.Sc., and Ph.D. degrees, all in computer science, from the University of British Columbia, Canada. Currently, he is an Assistant Professor at the University of Manitoba, Canada. His research interests include the areas of databases, data mining, and data warehousing. His work has been published in refereed journals and conferences such as ACM Transactions on Database Systems (TODS), IEEE International Conference on Data Engineering (ICDE), and IEEE International Conference on Data Mining (ICDM)

Quamrul I. Khan received his B.Sc. degree in computer science from North South University, Bangladesh, in 2001. He then worked as a Test Engineer and a Software Engineer for a few years before he started his current M.Sc. degree program in computer science at the University of Manitoba under the academic supervision of Dr. C. K.-S. Leung.

Zhan Li received her B.Eng. degree in computer engineering from Harbin Engineering University, China, in 2002. Currently, she is pursuing her M.Sc. degree in computer science at the University of Manitoba under the academic supervision of Dr. C. K.-S. Leung.

Tariqul Hoque received his B.Sc. degree in computer science from North South University, Bangladesh, in 2001. Currently, he is pursuing his M.Sc. degree in computer science at the University of Manitoba under the academic supervision of Dr. C. K.-S. Leung.

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Leung, C.KS., Khan, Q.I., Li, Z. et al. CanTree: a canonical-order tree for incremental frequent-pattern mining. Knowl Inf Syst 11, 287–311 (2007). https://doi.org/10.1007/s10115-006-0032-8

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