Discovery of Outlier from Database using Different Clustering Algorithms

Data mining is the process of extracting valuable and important data from a large or massive set of data. There are several techniques existing for data extraction. Clustering is one of the techniques amongst them. In clustering technique, we form the group of similar objects (similarity in terms of distance or there may be any other factor). Outlier detection is one of the major issues in data mining. Outlier represents that data which possess different behavior from others. Therefore, it is important to detect outlier from the extracted data. There are so many techniques existing to detect outlier but Clustering is one of the efficient techniques. In this paper, I have compared the result of different techniques with the result of Clustering techniques in terms of time complexity and proposed a new solution by adding fuzziness to already existing Clustering techniques.