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ABSTRACT

Fast retrieval of the relevant information from the databases has always been a significant issue. Different techniques have been developed for this purpose, one of them is Data Clustering. In this paper Data Clustering is discussed along with its two traditional approaches and their analysis. Some applications of Data Clustering like Data Mining using Data Clustering and Similarity Searching in Medial Image Databases are also discussed along with a case study of Microsoft Windows NT Operating system. The implementation of clustering in NT is also discussed.

INTRODUCTION
Data clustering is a method in which we make cluster of objects that are somehow similar in characteristics. The criterion for checking the similarity is implementation dependent.

Clustering is often confused with classification, but there is some difference between the two. In classification the objects are assigned to pre defined classes, whereas in clustering the classes are also to be defined.

Precisely, Data Clustering is a technique in which, the information that is logically similar is physically stored together. In order to increase the efficiency in the database systems the number of disk accesses are to be minimized. In clustering the objects of similar properties are placed in one class of objects and a single access to the disk makes the entire class available.