COMPARATIVE STUDY OF VARIOUS CLUSTERING TECHNIQUES?

Clustering is a process of dividing the data into groups of similar objects and dissimilar ones from other objects. Representation of data by fewer clusters necessarily loses fine details, but achieves simplification. Data is model by its clusters. Clustering plays an significant part in applications of data mining such as scientific data exploration, information retrieval, text mining, city-planning, earthquake studies, marketing, spatial database applications, Web analysis, marketing, medical diagnostics, computational biology, etc. Clustering plays a role of active research in several fields such as statistics, pattern recognition and machine learning. Data mining adds complications to very large datasets with many attributes of different types to clustering. Unique computational requirements are imposed on relevant clustering algorithms. A variety of clustering algorithms have recently emerged that meet the various requirements and were successfully applied to many real-life data mining problems.