Survey of Different Partition Clustering Algorithms and their Comparative Studies

Abstract

Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging in different groups are dissimilar. Clustering is an unsupervised learning technique. Data clustering is the subject of active research in several fields such as statistics, pattern recognition and machine learning. From a practical perspective clustering plays an outstanding role in data mining applications in many domains. The main advantage of clustering is that interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms. A variety of algorithms have recently emerged that meet these requirements and were successfully applied to real-life data mining problems. They are subject of this survey. Also, this survey explores the behavior of some of the partition based clustering algorithms.

 

Keywords: Clustering algorithms, K-mean clustering, K-medoids clustering, fuzzy C- mean clustering, k-attractor clustering.