DBSCAN BASED SEED INITIALIZATION OF K-MEANS ALGORITHM

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Published: Jun 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i5.3471
Keywords:
Gap statistics, silhouette, jump method, centroids, k means, euclidean distance, density reachable, Dbscan

Main Article Content

sameer koul
Islamia college of science and commerce

Abstract

This paper proposes effective approach to overcome the problem of finding initial number of clusters for Supervised Data mining algorithms. We present critical review of various approaches that finds the optimal number of clusters for clustering algorithms. In this paper we have used Dbscan algorithms to obtain initial seeds for basic k-means algorithm. To evaluate the proposed approache we have used iris data set, liver disorder dataset and seed dataset.

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Article Details

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Vol. 8 No. 5 (2017): May-June 2017
Section
Articles

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Author Biography

sameer koul, Islamia college of science and commerce

assistant proffesor

References

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