AN ENHANCED ALGORITHM FOR IMPROVED CLUSTER GENERATION TO REMOVE OUTLIER’S RATIO FOR LARGE DATASETS IN DATA MINING

Abstract

existing studies in data mining focus on Outlier detection on data with single clustering algorithm mostly.
There are lots of methods available in data mining to detect the outlier by making the clusters of data and then detect the
outlier from them. Outlier can be reduced if we improve the clustering. The values or objects that are similar to each
other are organized in group it’s called cluster and the values or objects that do not comply with the model or general
behavior of the data these data objects called outliers. Outliers detect by clustering. We make algorithm that will be
generate the percentage value of cluster and the outliers and its compulsory to total no of cluster percentage are greater
than the total no of outlier percentage. If the cluster are not more than outliers then algorithm will be improved the total
no of cluster and reduce the outliers. The output of the algorithm will be generating total original objects. If the no of
input objects and no of output objects are not same then we assume that some error occur in the algorithm.