Call for Paper - May 2023 Edition
IJCA solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 20, 2023. Read More

An Algorithm for Automated View Reduction in Weighted Clustering of Multiview Data

Print
PDF
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 16
Year of Publication: 2014
Authors:
N. Aparna
M. Kalaiarasu
10.5120/15291-3942

N Aparna and M Kalaiarasu. Article: An Algorithm for Automated View Reduction in Weighted Clustering of Multiview Data. International Journal of Computer Applications 87(16):12-17, February 2014. Full text available. BibTeX

@article{key:article,
	author = {N. Aparna and M. Kalaiarasu},
	title = {Article: An Algorithm for Automated View Reduction in Weighted Clustering of Multiview Data},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {87},
	number = {16},
	pages = {12-17},
	month = {February},
	note = {Full text available}
}

Abstract

Clustering multiview data is one of the major research topics in the area of data mining. Multiview data can be defined as instances that can be viewed differently from different viewpoints. Usually while clustering data the differences among views are ignored. In this paper, a new algorithm for clustering multiview data is proposed. Here, both view and variable weights are computed simultaneously. The view weight is used to determine the closeness or density of view. Those views which have a weight less than a predefined value are considered insignificant and are eliminated. Variable weight is used to identify the significance of each variable. In order to determine the cluster of objects both these weights are used in the distance function. In the proposed method, enhancement to the usual iterative k-means is done so that it automatically computes both view and variable weights.

References

  • S. Bickel and T. Scheffer,2004, "Multi-view Clustering," Proc. IEEE Fourth Int'l Conf. Data Mining, pp. 19-26.
  • D. Zhou and C. Burges 2007 "Spectral Clustering and Transductive Learning with Multiple Views," Proc. 24th Int'l Conf. Machine Learning, pp. 1159-1166.
  • G. Tzortzis and C. Likas, 2010 "Multiple View Clustering Using a Weighted Combination of Exemplar-Based Mixture Models," IEEE Trans. Neural Networks, vol. 21, no. 12, pp. 1925-1936.
  • R. Gnanadesikan, J. Kettenring, and S. Tsao, 1995 "Weighting and Selection of Variables for Cluster Analysis," J. Classification,vol. 12, pp. 113-136.
  • G. De Soete, 1986 "Optimal Variable Weighting for Ultrametric and Additive Tree Clustering," Quality and Quantity, vol. 20, pp. 169-180.
  • E. Fowlkes, R. Gnanadesikan, and J. Kettenring,1988 "Variable Selection in Clustering," J. Classification, vol. 5, pp. 205-228.
  • B. Long, P. Yu, and Z. Zhang, 2008"A General Model for Multiple View Unsupervised Learning," Proc. Eighth SIAM Int'l Conf. Data Mining (SDM '08.
  • V. R. de Sa, "Spectral Clustering with Two Views," 2005 Proc. IEEE 22nd Int'l Workshop Learning with Multiple Views (ICML),pp. 20-27.
  • Z. Huang, M. Ng, H. Rong, and Z. Li, 2005"Automated Variable Weighting in k-Means Type Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 657-668.
  • L. Jing, M. Ng, and Z. Huang, 2007 "An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data," IEEE Trans. Knowledge and Data Eng. , vol. 19, no. 8, pp. 1026-1041.
  • W. DeSarbo, J. Carroll, L. Clark, and P. Green, 1984 "Synthesized Clustering: A Method for Amalgamating Clustering Bases with Differential Weighting Variables," Psychometrika, vol. 49, no. 1,pp. 57-78.
  • A. Frank and A. Asuncion, 2010 "UCI Machine Learning Repository," http://archive. ics. uci. edu/ml .