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With the increasing demand of IT and subsequent growth in this sector, the high- dimensional data came into existence. Data Mining plays an important role in analyzing and extracting the useful information. The key information which is extracted from a huge pool of data is useful for decision makers. Clustering, one of the techniques of data mining is the mostly used methods of analyzing the data. In this paper, the approach of Kohonen SOM and K-Means and HAC are discussed. After that these three methods are used for analyzing the academic data set of the faculty members of particular university. Finally a comparative analysis of these algorithms are done against some parameters like number of clusters, error rate and accessing rate, etc. Â This work will present new and improved results from large-scale datasets.

Comparative Analysis of Kohonen-SOM and K-Means data mining algorithms based on Academic Activities