Description

Data mining also known as Knowledge Discovery in Data bases (KDD) is the process of extracting use full information from data in databases. Data mining techniques involves sophisticated algorithms clustering, classification, association detection, pattern recognitions. Classification is a data mining (machine learning) technique used to predict group membership for data instances. Clustering is an unsupervised classification of patterns into clusters. Classification has been identified as an important problem in the emerging field of data mining. Thus the proposed work focuses on integrating clustering and classification. For classifying large data set Decision tree classifiers are used. In this paper for clustering and classification In Data Mining Clustering and Classification are two important techniques. In this paper we make use of large database (Diabetes dataset containing) to perform an integration of clustering and classification technique. We compared the results of simple classification technique (J48 classifier) with the results of integration of clustering (X-Means) and classification (J48) techniques based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis) a data mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate even when the dataset contains missing values.