Outlier Detection and Analysis using Hybrid Approach for Mixed datasets

There are several approaches of outlier detection employed in many study areas amongst which distance based and density based outlier detection techniques have gathered most attention of researchers.So we are using hybrid of these two methods.The existing system uses distance based method for outlier detection and K-means  as clustering method.But distance based method has limitation that it fails for non-uniform datasets.The k-means method requires number of clusters to form as input which is difficult for real life datasets which contains millions of attributes and rows.So we move to proposed model.The proposed model uses hybrid of distance and density outlier detection methods and weighted squuzer method for clustering.Most of the models deals with only single datasets.Here the project deals with mixed datasets.Future scope will be to handle dyanamic data.

Keywords—outlierdetection,weighted squeezer clustering, hybrid, distance ,density based, k-means, mixed datasets.