Clustering Algorithm Based Outlier Detection in Data Mining
Many data mining applications, the most important step is detecting outliers in a dataset. Outlier detection for data mining is usually based on distance, clustering and spatial methods. This study deals with locating outliers in large and multidimensional data sets with k means, k medoids and Fuzzy C Means clustering methods. The k means clustering algorithm partitions a dataset into the amount of clusters and then the effects are used to key out the outliers from each cluster, using any one of the outlier detection methods. The k means clustering algorithm is improved in three ways. The initial step by using a different distance metric. The second and third improvements are carried forward by automating the method of estimating 'k' value and primary seed selection using the improved clustering algorithm. The calculated results show that classification accuracy, speeds are enhanced and normalized root mean square error is scaled down.