Outlier Detection for Multidimensional Medical Data
Journal Title: International Journal of Computer Science and Information Technologies - Year 2011, Vol 2, Issue 1
Abstract
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.
Authors and Affiliations
Anbarasi. M. S , Ghaayathri. S , Kamaleswari. R , Abirami. I
Keywords
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