Databases today can range in size more than terabytes. Within these masses of data lies hidden information of strategic importance. But when there are so many trees, how do we draw meaningful conclusions about the forest? The newest answer is data mining, which is being used both to increase revenues and to reduce costs. Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions. The research uses social networking data set for pattern recognition, because it is one of the emerging application areas in data mining. We are using the Facebook 100 dataset and applying the Bisecting KMeans algorithm on it, by which we would get better clustering results. Bisecting KMeans first bisects the data into two parts and selects the part with greater number of elements, then applies clustering on it again. This goes on till we have N Number of clusters. We would apply this to our dataset to get desired results. With this we are going to compare Bisecting K Mean algorithm with other data mining algorithm. And finally we are going to find out different pattern from social networking dataset.Keywords: SNS, bisecting KMean, Social networking, KMean
