A Peculiar Approach for Detection of Cluster Outlier in High Dimensional Data

As data is becoming huge and available in diverse formats, we need algorithms enabling data to be clustered and detecting the outliers. We have many methods of dealing with the outlier values Outliers are those unprecedented values that dont go with the structure of the data set and hence lead to wrong results or confusion. Data mining algorithms work by first detecting the outlier values and then either filling these values or removes these values. After thorough search, we found that outliers and clusters have a connection between them Hence its very useful to deal with both these concepts for efficient 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. Using this algorithm we have made clusters based on the relation between cluster and the inter dependence between the cluster and the outliers. , running until termination is achieved