Graphical method to find optimal cluster centroid for two-variable linear functions of concept-drift categorical data

Authors:
K. Reddy Madhavi;A. Vinaya Babu;A. Anand Rao;S. Viswanadha Raju;C. N. Rau
Affiliations:
Research Scholar, JNIAS/JNTUA, Ananthpur;JNTUHCE, Hyderabad;JNTUACE, Ananthpur;JNTUH, Hyderabad;SVDC, Kadapa
Venue:
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Year:
2012

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Abstract

Identification of useful clusters in large datasets has attracted considerable interest in clustering process. Since data in the World Wide Web is increasing exponentially that affects on clustering accuracy and decision making, change in the concept between every cluster occurs named concept drift. The new data must be assigned to any one of generated clusters called data labeling. To say that data labeling was performed well the clusters must be efficient. Selecting initial cluster center (centroid) is the key factor that has high affection in generating effective clusters. So we are proposing approaches. Ore previous work was concentrated on finding minimum point that act as initial cluster centroid for single variable functions. This paper introduces a two methods simplex and graphical methods to select optimal cluster centroid for the linear functions of two variables and concludes with preferable method for functions of same nature. After finding initial cluster centroid we can then apply any existing clustering algorithm.