A Novel Approach for Cluster Outlier Detection in High Dimensional Data

Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. There are also several types of approaches designed for outlier detection. Outliers are those data objects that do not fulfill with the common behavior or model of the data. Many data mining algorithms try to reduce the effects of outliers or remove them all together. We investigated that in many different conditions clusters and outliers whose meanings are connected to each other, especially for those data sets which contains some noise. So it is important to deal clusters and outliers as concepts of the same significance in data analysis. So in this paper we introduce an algorithm which is based on k means [1] for the detection of clusters and outliers that aim to detect the clusters and the outliers in a different view for those data sets which contains some noise. In this algorithm clusters are detected and managed according to the intra-relationship within the clusters and inter-relationship between the clusters and the outliers. The whole management and modification of the clusters and outliers are done repeatedly just before a certain termination is reached