Mining Patterns for Clustering Using Modified K-Means and SVM (Support Vector Machine)

Data mining can be termed as a process of extracting patterns (knowledge) and posing query from data. Stored in database. Classification is one among of its concept and techniques.  This research article is proposing a novel hybrid mining approach by using modified K-Means and Support vector machine algorithm. Modified K-Means utilized here for making the clusters from given dataset and SVM is utilized for classification (on clustered dataset obtained from modified K-means clustering). Experiments are performed over different datasets which are taken from UCI repository. Datasets which are used for comparing clustering algorithm are provided in Table 1 along with their details. Evaluations are done on different datasets of following parameters: Accuracy obtained from new algorithm and confusing matrix which is being created for every dataset. Additionally, proposed algorithms provide better result than other.