An Efficient Mechanism for Data Mining with Clustering and Classification Analysis as a Hybrid Approach
Appearance of modern techniques for scientific records collection has resulted in big scale accumulation of data pertaining to various fields. Predictable database querying methods are insufficient to extract functional information from enormous data banks. In this paper, the authors are using clustering with classification and decision tree methods to mining the data by using hybrid algorithms like EM, K-means and HAC algorithms from clustering, J48 and C4.5 algorithms from decision making and it can generate the improved outcome than the conventional algorithms. It also performs the proportional study of these algorithms to acquire elevated accuracy. This contrast is capable to discover clusters in huge high dimensional spaces proficiently.