This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.