Random Articles
Reseach Article
An Accurate Revelation of the Similarity between Clusters
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 78 - Number 10 |
| Year of Publication: 2013 |
| Authors: A. Veera Mahendra, S. M. Farooq |
10.5120/13525-1220
|
A. Veera Mahendra, S. M. Farooq . An Accurate Revelation of the Similarity between Clusters. International Journal of Computer Applications. 78, 10 ( September 2013), 16-20. DOI=10.5120/13525-1220
Abstract
The structure of the data set playing a vital role in datamining. In concept of datamining information recovery and pattern identification nothing but data clustering. There are multiple clustering algorithms have been commenced to clustering categorical data. Unfortunately these algorithms created an incomplete information. In recent times cluster ensembles have come out as an essential solution to overcome these limitations and to get the excellence results for clustering. A Link-Based similarity measure is proposed to guess unknown values from a link network of clusters and bridges the gap among the task of data clustering and that link examination. It also improves the ability of ensemble methodology for categorical data. A new Link-Based cluster ensemble approach is commenced which is well-organized than the previous model, where a binary cluster association matrix, like matrix is used to create the cluster ensembles. These cluster ensembles have impurity information, to overcome these problem Link-Based similarity algorithm is used to generate an accurate pure clusters.
References
- A Link-Based Cluster Ensemble Approach for Categorical Data Clustering Natthakan Iam-On, Tossapon Boongoen, Simon Garrett, and Chris Price
- L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Publishers, 1990.
- A. K. Jain and R. C. Dubes, Algorithms for Clustering. Prentice-Hall, 1998.
- P. Zhang, X. Wang, and P. X. Song, "Clustering Categorical Data Based on Distance Vectors," The J. Am. Statistical Assoc. , vol. 101, no. 473, pp. 355-367, 2006.
- S. Guha, R. Rastogi, and K. Shim, "ROCK: A Robust Clustering Algorithm for Categorical Attributes," Information Systems, vol. 25, no. 5, pp. 345-366, 2000.
- G. Jeh and J. Widom, "Simrank: A Measure of Structural-Context Similarity," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 538-543, 2002.
- Z. Huang, "Extensions to the K-Means Algorithm for Clustering Large Data Sets with Categorical Values," Data Mining and Knowledge Discovery, vol. 2, pp. 283-304, 1998.
- C. Domeniconi and M. Al-Razgan, "Weighted Cluster Ensembles:Methods and Analysis," ACM Trans. Knowledge Discovery from Data, vol. 2, no. 4, pp. 1-40, 2009.
- X. Z. Fern and C. E. Brodley, "Solving Cluster Ensemble Problems by Bipartite Graph Partitioning," Proc. Int'l Conf. Machine Learning (ICML), pp. 36-43, 2004.
- A. Strehl and J. Ghosh, "Cluster Ensembles: A Knowledge Reuse Framework for Combining Multiple Partitions," J. Machine Learning Research, vol. 3, pp. 583-617, 2002.
- G. Karypis and V. Kumar, "Multilevel K-Way Partitioning Scheme for Irregular Graphs," J. Parallel Distributed Computing, vol. 48, no. 1, pp. 96-129, 1998.
- A. Ng, M. Jordan, and Y. Weiss, "On Spectral Clustering: Analysis and an Algorithm," Advances in Neural Information Processing Systems, vol. 14, pp. 849-856, 2001.
Index Terms
Keywords