A Review for Data Clustering Techniques

Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. Clustering is a process of grouping objects with similar properties. Any cluster should exhibit two main properties; low inter-class similarity and high intra-class similarity. The goal of this survey is to provide a comprehensive review of different clustering techniques in data mining. Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery. This paper gives an overview of different clustering algorithms used in large data sets. In addition the paper also describes the efficiency of Self-Organized Map (SOM) algorithm in enhancing the mixed data clustering.