Open Access Open Access  Restricted Access Subscription or Fee Access

Clustering In Data Mining Using Modified KMeans Algorithm

T. Senthil Prakash, K. Maheswari, V. Kamaraj

Abstract


The main objective of clustering is to find similarities between data by using Cluster analysis method. Clustering is a type of multivariate statistical analysis. It groups similar samples together to assist in understanding the relationships that exist between them. Cluster analysis is based on mathematical formulation of a measure of similarity. Clustering is a type of multivariate statistical analysis, unsupervised classification analysis, or numerical taxonomy. The main objective of clustering is to find similarities between experiments or genes, and then group similar samples or genes together to assist in understanding relationships that might exist among them. It is a technique for sorting cases (genes, samples, etc.) into groups, or clusters, so that the degree of association is strong between members of the same cluster and weak between members of different clusters. Data subsets of genes or samples get grouped together (clustered) based on their similarities. Cluster Analysis can be used as a general data reduction tool to develop clusters (or) subgroups of data that are more manageable than individual observations. It is used for taxonomy for related animals, insects or plants. It suggests statistical models with which to describe populations. It is used to indicate rules for assigning new cases to classes for identification and diagnostic


Keywords


Statistical Analysis, Cluster Analysis, Statistical Models, Hierarchical Methods, Divisive Clustering

Full Text:

PDF

References


G.L. Nemhauser and L.A. Wosley, “Integer and CombinatorialOptimization,” Wiley-Interscience Series in Discrete Math. And Optimization, 1999.

B. Fortz and M. Thorup, “Optimizing OSPF/IS-IS Weights in a Changing World,” IEEE J. Selected Areas in Comm., vol. 20, no. 4,pp. 756-767, May 2002.

E. Wong, A. Chan, and T. Yum, “Analysis of Rerouting in Circuit- Switched Networks,” IEEE/ACM Trans. Networking, vol. 8, no. 3,pp. 419-427, June 2000.

K.-C. Lee and V.O.K. Li, “A Wavelength Rerouting Algorithm inWide- Area All-Optical Networks,” IEEE J. Lightwave Technology,vol. 14, no. 6, pp. 1218-1229, June 1996.

A. Donner, M. Berioli, and M. Werner, “MPLS-Based SatelliteConstellation Networks,” IEEE J. Selected Areas in Comm., vol. 22,no. 3, pp. 438-448, Apr. 2004.

Y. Yang, J. Kramer, D. Papadias, and B. Seeger, “HybMig: A Hybrid Approach to Dynamic Plan Migration for Continuous Queries,” IEEE Trans. Knowledge and Data Eng., vol. 19, no. 3, pp. 398-411, Mar. 2007.

Data warehousing & data mining and OLAP by Alex Berson & Stephen J Smith.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.