Neural Information Processing

Title

Neural Information Processing

Role

Editors: Sabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu

Contributing authors: Haishuai Wang, Peng Zhang, Jia Wu, Shirui Pan

Files

Document Type

Conference Proceeding

Description/Summary

Haishuai Wang (with Peng Zhang, Jia Wu, Shirui Pan) is a contributing author, "Mining Top-k Minimal Redundancy Frequent Patterns over Uncertain Databases."

Book introduction:

The four volume set LNCS 9489, LNCS 9490, LNCS 9491, and LNCS 9492 constitutes the proceedings of the 22nd International Conference on Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The 4 volumes represent topical sections containing articles on Learning Algorithms and Classification Systems; Artificial Intelligence and Neural Networks: Theory, Design, and Applications; Image and Signal Processing; and Intelligent Social Networks.

Paper abstract:

Frequent pattern mining from uncertain data has been paid closed attention due to most of the real life databases contain data with uncertainty. Several approaches have been proposed for mining high significance frequent itemsets over uncertain data, however, previous algorithms yield many redundant frequent itemsets and require to set an appropriate user specified threshold which is difficult for users. In this paper, we formally define the problem of top-k minimal redundancy probabilistic frequent pattern mining, which targets to identify top-k patterns with high-significance and low-redundancy simultaneously from uncertain data. We first design uncertain pattern correlation based on Pearson correlation coefficient, which considers pattern uncertainty. Moreover, we present a new algorithm, UTFP, to mine top-k minimal redundancy frequent patterns of length no less than minimum length min_l without setting threshold. We further propose a set of strategies to prune and reduce search space. Experimental results demonstrate that the proposed algorithm achieves good performance in terms of finding top-k frequent patterns with low redundancy on probabilistic data. Our method represents the first research endeavor for probabilistic data based top-k correlated pattern mining.

ISBN

9783319265605

Publication Date

2015

Publication Information

Wang H., Zhang P., Wu J., Pan S. (2015) Mining Top-k Minimal Redundancy Frequent Patterns over Uncertain Databases. In: Arik S., Huang T., Lai W., Liu Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science, vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_14

Comments

Copyright 2015 Springer International Publishing Switzerland

Neural Information Processing

@ the DiMenna-Nyselius Library

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