Data clustering is a highly valuable field of computational statistics and data mining. Data clustering can be considered as the most important unsupervised learning technique as it deals with finding a structure in a collection of unlabeled data. A Clustering is division of data into similar objects. A major difficulty in the design of data clustering algorithms is that, in majority of applications, new data are dynamically appended into an existing database and it is not feasible to perform data clustering from scratch every time new data instances get added up in the database. The development of clustering algorithms which handle the incremental updating of data points is known as an Incremental clustering. In this paper authors have reviewed Partition based clustering methods mainly, K-means & DBSCAN and provided a detailed comparison of Traditional clustering and Incremental clustering method for both.

Traditional Clustering Incremental Clustering K-Means Clustering