Soft subspace clustering with an improved feature weight self-adjustment mechanism

Journal of Ambient Intelligence and Humanized Computing

Volumn 3, Issue 1, 2012, Pages 39-49

  Guo, Gongde ^a   Chen, Si ^a   Chen, Lifei ^a  

^a FUJIAN NORMAL UNIVERSITY   (China)

Author keywords

Clustering; Data mining; Feature weighting; High dimensional data; Subspace

Indexed keywords

CLUSTERING; CLUSTERING QUALITY; DATA SETS; FEATURE WEIGHT; FEATURE WEIGHTING; HIGH DIMENSIONAL DATA; HIGH DIMENSIONALITY; PARAMETER VALUES; REAL-WORLD APPLICATION; SELF-ADJUSTMENT; SUBSPACE; SUBSPACE CLUSTERING; TRADITIONAL CLUSTERING;

DATA MINING;

CLUSTERING ALGORITHMS;

EID: 84863262331     PISSN: 18685137     EISSN: 18685145     Source Type: Journal    
DOI: 10.1007/s13042-011-0038-8     Document Type: Article

References (25)

1. Steinbach M, Ertöz L, Kumar V (2004) The challenges of clustering high dimensional data. New directions in statistical physics: econophysics, bioinformatics, and pattern recognition, pp 273-308.
2. Han JW, Kamber M (2007) Data mining: concepts and techniques, 2nd edn. China Machine Press, Beijing.
3. Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inform Technol Decis Making 5(4): 597-604.
4. Huang JZ, Ng MK, Rong H, Li Z (2005) Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell 27(5): 1-12.
5. Wang LJ (2010) An improved multiple fuzzy NNC system based on mutual information and fuzzy integral. Int J Mach Learn Cybern 2(1): 25-36.
6. Hu QH, Pan W, An S, Ma PJ, Wei JM (2010) An efficient gene selection technique for cancer recognition based on neighborhood mutual information. Int J Mach Learn Cybern 1(1-4): 63-74.
7. Shah NH, Shukla KT (2010) Optimal production schedule in declining market for an imperfect production system. Int J Mach Learn Cybern 1(1-4): 89-99.
8. Tsai CY, Chiu CC (2008) Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm. Comput Stat Data Anal 52: 4658-4672.
9. Aggarwal CC, Wolf JL, Yu PS, Procopiuc C, Park JS (1999) Fast algorithm for projected clustering. In: Proceedings of the ACM SIGMOD, pp 61-72.
10. Woo KG, Lee JH, Kim MH, Lee YJ (2004) FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting. Inform Softw Technol 46(4): 255-271.
11. Yip KY, Cheung DW, Ng MK (2004) A practical projected clustering algorithm. IEEE Trans Knowl Data Eng 16(11): 1387-1397.
12. Parsons L, Haque E, Liu H (2004) Subspace clustering for high dimensional data: a review. ACM SIGKDD Explor Newsl 6(1): 90-105.
13. Chan EY, Ching WK, Ng MK, Huang JZ (2004) An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recogn 37(5): 943-952.
14. Jing L, Ng MK, Huang JZ (2007) An entropy weighting K-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans Knowl Data Eng 19(8): 1-16.
15. Domeniconi C, Gunopulos D, Ma S, Yan B, Al-Razgan M, Papadopoulos D (2007) Locally adaptive metrics for clustering high dimensional data. Data Min Knowl Disc 14(1): 63-97.
16. Jing L, Ng MK, Xu J, Huang JZ (2005) Subspace clustering of text documents with feature weighting k-means algorithm. Adv Knowl Discov Data Mining 3518: 802-812.
17. Gan G, Wu J, Yang Z (2006) A fuzzy subspace algorithm for clustering high dimensional data. In: Li X, Zaiane O, Li Z (eds) Lecture notes in artificial intelligence 4093. Springer, Berlin, pp 271-278.
18. Gan G, Wu J (2008) A convergence theorem for the fuzzy subspace clustering algorithm. Pattern Recogn 41: 1939-1947.
19. Deng Z, Choi KS, Chung FL, Wang S (2010) Enhanced soft subspace clustering integrating within-cluster and between-cluster information. Pattern Recogn 43: 767-781.
20. Domeniconi C, Papadopoulos D, Gunopulos D, Ma S (2004) Subspace clustering of high dimensional data, In: Proceedings of the SIAM international conference on data mining.
21. Friedman JH, Meulman JJ (2004) Clustering objects on subsets of attributes. J R Stat Soc B 66(4): 815-849.
22. Frigui H, Nasraoui O (2004) Unsupervised learning of prototypes and attribute weights. Pattern Recogn 37(3): 567-581.
23. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn edn. Morgan Kaufmann, San Francisc.
24. Asuncion A, Newman D J (2007) UCI Machine Learning Repository. School of Information and Computer Science, CA: University of California, Irvine. http://www.ics.uci.edu/~mlearn/MLRepository.html.
25. Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams, In Proc. of ACM International Conference on Knowledge Discovery and Data Mining, ACM Press: 97-106.