ABSTRACT
284The data clustering algorithms have applications in many areas of data mining, pattern recognition, information retrieval, and image processing. The enormous amount of data generated every day by different sources and their structure and complexity have put forth a major challenge for researchers in the present day for their analysis and mining fruitful rules or finding patterns from them. The traditional clustering algorithms do not have the capability to handle such situations. Developing scalable computing platforms has turned out to be in high demand. The uncertainty hidden in these large data sets has further complicated situations like their counterparts in normal data sets. Researchers are trying to follow the incremental approach in improving the existing data clustering algorithms to make them fit to this challenging situation. However, there have been limited efforts so far. The main objective of this chapter is to present the existing data clustering algorithms available in the literature with a critical review and present some more possibilities in this direction.
