Cluster Validity Analysis on Soft Set Based Clustering
D. Molodtsov, “Soft set theory-first results,” in Computer and Mathematics with Applications, vol. 37, no. 4/5, pp. 19-31, 1999.
R. Mamat, M. M. Deris and T. Herawan, “MAR – Maximum attribute relative of soft-set for partition attribute selection,” Knowledge Based System, vol. 52, pp. 11-20, 2013.
Z. Pawlak, “Hard and soft sets,” in RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery, 1993, pp. 130-135.
D. Pei, and D. Miao, “From soft sets to information systems,” in 2005 IEEE International Conference on Granular Computing, vol. 2, 2005, pp. 617-621.
Z. Pawlak, “Rough sets,” International Journal of Computer and Information Sciences, vol. 11, no. 5, pp. 342-356, 1982.
Z. Pawlak, Rough Sets: Theoretical Aspect of Reasoning about Data. Netherlands: Springer, 1991.
Y. Y. Yao, and N. Zhong, “Granular computing using information tables,” in Data Mining, Rough Sets and Granular Computing, T. Y. Lin, Y. Y. Yao, and L. A. Zadeh, Eds. Heidelberg: Physica, 2002, pp. 102-124.
Z. Pawlak, “Rough set approach to knowledge-based decision support,” European Journal of Operational Research, vol. 99, no. 1, pp. 48-57. 1997.
Z. Pawlak, and A. Skowron, “Rudiments of rough set,” Information Sciences, vol. 177, no. 1, pp. 3-27, 2002.
T. Herawan, and M. M. Deris, “On multi-soft sets construction in information systems,” in Emerging Intelligent Computing Technology and Applications with Aspects of Artificial Intelligence, D.-S. Huang, K.-H. Jo, H.-H. Lee, H.-J. Kang, and V. Bevilacqua, Eds. Berlin, Heidelberg: Springer, 2009, pp. 101-110.
S. C. Sripada, and S. M. Rao, “Comparison of purity and entropy of kmeans clustering and c-means clustering,” Indian Journal of Computer Science and Engineering, vol. 2, no. 3, pp. 343-346. 2011.
C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, pp. 379-423 and 623-659, 1948.
H. Xiong, M. Steinbach, P. N. Tan, and V. Kumar, “HICAP: Hierachical clustering with pattern preservation,” in Proceeding of the 4th SIAM International Conference on Data Mining, 2004, pp. 279-290.
Y. Zhao, G. Karyis, and U. Fayyad, “Hierachical clustering algorithms for document datasets,” Data Mining and Knowledge Discovery, vol. 10, no. 2, pp. 141-168, 2005.
H. Kim, and H. Park, “Sparse non-negative matrix factorization via alternating non-negative constrained least squares for microarray data analysis,” Bioinformatics, vol. 23, no. 12, pp. 1495-1502, 2007.
L. Hubert, and P. Arabie, “Comparing partitions,” Journal of Classification, vol. 2, no. 1, pp. 193-218, 1985.
W. M. Rand, “Objective criteria for the evaluation of clustering methods,” Journal of American Statistical Association, vol. 66, no. 36, pp. 846-850, 1971.
K. Y. Yeung, and W. L. Ruzzo, “Principal components analysis for clustering gene expression data,” Bioinformatics, vol. 17, no. 9, pp. 763-774, 2001.
M. J. Santos, and M. Embrechts, “On the use of the adjusted rand index as a metric for evaluating supervised classification,” Artificial Neural Networks – ICANN 2009, C. Alippi, M. Polycarpou, C. Panayiotou, and G. Ellinas, Eds. Berlin, Heidelberg: Springer, vol. 5769, 2009, pp.175- 184.
C. J. V. Rijssbergen, “Foundation of evaluation,” Journal of Documentation, vol. 30, no. 4, pp. 365-373, 1974.
R. Marxer, P. Holonowicz, P. Purwins, and A. H. Hazan, “An fmeasure for evaluating of unsupervised clustering with non-determined number of clusters,” Technical Report, UniversitatPempeuFabra, 2008.
T. Herawan, M. M. Deris, and J. H. Abawajy, “A rough set approach for selecting clustering attribute,” Knowledge Based System, vol. 23, no. 3, pp. 220-231, 2010.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.