Outlier Detection for Multidimensional Medical Data

The knowledge-rich nature of the Medical Information domain has made it an ideal environment where knowledge on data mining should have to be unearthed from large data collection for dialysis’ of growing unknown diseases. Outlier detection is an important research problem that aims to find objects that are considerably dissimilar, exceptional and inconsistent in the database. Medical application is a high dimensional domain hence determining outliers is found to be very tedious due to curse of dimensionality. Most of the existing outlier detection methods detect the so-called point outliers from vector-like data sets. In this paper, clustering technique is used to cluster for multidimensional data. The draw back in clustering is overcome by auto K-generation as first process. Then the outliers are deducted by Thompson’s Tau method which is further enhanced by max-flow min-cut theorem to find the uniqueness of outliers in multidimensional Medical data. Keywords— Outlier detection, Multi dimensional data, Outliers, Clustering, Min cut theorem, k means. I.INTRODUCTION Due to incredible growth of medical dataset, conventional data base querying methods are inadequate to extract useful information, so researches nowadays are focussed to develop new techniques to meet the raised requirements. The increase in dimensionality of data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of attributes. There are various origins of outliers. With the growth of the medical dataset day by day, the process of determining outliers becomes more complex and tedious. K-means is a well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroids. In this system, the enhanced auto Kgeneration for K-means is done as the first step. But its output is quite sensitive to initial positions of cluster centers. Again, the number of distance calculations increases exponentially with the increase of the dimensionality of the data. As quality of the final clusters heavily depends on the selection of the initial centroids, a new method is used to choose such data objects as initial centroids. After clustering left out points are deducted by Thompson’s Tau method. These points are determined and checked out if they can be accommodated in any of the cluster by max flow min-cut theorem. As a result, the number of outliers is minimised and uniqueness in outliers are deducted as a goal of this work. II.RELATED WORK (Sheng-Yi Jiang and Ai-Min Yang, 2009) [1] Have proposed a system which determines outliers in two stages. In first stage the dataset is clustered whereas in the second stage the gained clusters are categorised as “normal cluster” or “outlier cluster” according to an outlier factor, and finally confirm the outlier objects. (K. A. Abdul Nazeer and M. P. Sebastian,2009) [2] have proposed an efficient algorithm which determines the initial centroids rather than assigning arbitrary centriods. (Robert Tibshirani et al., 2003 [4]) have proposed a method to automatically generate the number of clusters as input to the k means clustering algorithm. (Jani Posio et al., 2008) [3] Have proposed a method for determining outliers in 2D temperature data.