Abstract: Data Mining is a very interesting area to mine the data for knowledge. Several techniques are available. A data mining is one of the fast growing research field which is used in a wide areas of applications. The data mining consists of classification algorithms, association algorithms and searching algorithms. 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 ID3 and C4.5 and CART algorithms from decision tree and it can produce the better results than the traditional algorithms. The comparative study of these algorithms to obtain which one is high accuracy, frequency, measure, procedure, Pruning, error rate and time complexities from the decision tree algorithm. At the same time the comparative study of these algorithms to obtain which one is quality, error rate, computation time, accessing time, performance, number of cluster, Map topology, type of software, type of data, and data set size from the clustering algorithm.
Key words: K-MEANS, SOM, HAC, ID3, C4.5, CART
Title: Comparative Study of different clustering and Decision Tree for Data Mining Algorithm
Author: Ms. A. Sivasankari, Mrs. S. Sudarvizhi, S. Radhika Amirtha Bai
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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