Fast Iterative Mining Using Sparsity-Inducing Loss Functions-Reference-Cited by-同舟云学术

Fast Iterative Mining Using Sparsity-Inducing Loss Functions

Published:2013 Issue:8 Volume:E96.D Page:1766-1773
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

SAIGO Hiroto¹,KASHIMA Hisashi²,TSUDA Koji³

Affiliation:

1. Kyushu Institute of Technology

2. University of Tokyo

3. Advanced Industrial Science and Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E96.D/8/E96.D_1766/_pdf

Reference22 articles.

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3. [3] M.J. Zaki, “Efficiently mining frequent trees in a forest: Algorithms and applications,” IEEE Trans. Knowl. Data Eng., vol.65, no.1-2, pp.33-52, 2005.

4. [4] J. Pei, J. Han, B. Mortazavi-asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, “Mining sequential patterns by pattern-growth: The prefixspan approach,” IEEE Trans. Knowl. Data Eng., vol.16, no.11, pp.1424-1440, 2004.

5. [5] X. Yan and J. Han, “gSpan: Graph-based substructure pattern mining,” Proc. 2002 IEEE International Conference on Data Mining, pp.721-724, IEEE Computer Society, 2002.