Coalescing Clustering and Classification

Abstract—Coalescing 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. < Final Year Projects > 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. 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.