Data Mining with Improved and Efficient Mechanism in Clustering Analysis and Decision Tree as a Hybrid Approach
Heena Sharma1, Navdeep Kaur Kaler2

1Heena Sharma, Research Scholar, M.Tech, Department of CSE, L.L.R.I.E.T Moga P.T.U, (Punjab), India.
2Navdeep Kaur Kaler, Assistant Professor, Department of CSE, L.L.R.I.E.T, Moga (Punjab), India.
Manuscript received on 15 April 2013 | Revised Manuscript received on 22 April 2013 | Manuscript Published on 30 April 2013 | PP: 58-60 | Volume-2 Issue-5, April 2013 | Retrieval Number: E0646032413/13©BEIESP
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Abstract: In this research, we are using clustering and decision tree methods to mine the data by using hybrid algorithms K-MEANS, SOM and HAC algorithms from clustering and CHAID and C4.5 algorithms from decision tree and it can produce the better results than the traditional algorithms. It also performs the comparative study of these algorithms to obtain high accuracy. Clustering method will use for make the clusters of similar groups to extract the easily features or properties and decision tree method will use for choose to decide the optimal decision to extract the valuable information.This comparison is able to find clusters in large high dimensional spaces efficiently. It is suitable for clustering in the full dimensional space as well as in subspaces. Experiments on both synthetic data and real-life data show that the technique is effective and also scales well for large high dimensional datasets.
Keywords: Clustering, Decision tree, HAC, SOM, C4.5, Data Mining, K-Means.

Scope of the Article: Clustering