Open AccessA System for Outlier Detection of High Dimensional Data
Author(s) -
Bharat Gupta,
Durga Toshniwal
Publication year - 2012
Publication title -
international journal of computer science and informatics
Language(s) - English
Resource type - Journals
ISSN - 2231-5292
DOI - 10.47893/ijcsi.2012.1037
Subject(s) - outlier , anomaly detection , linear subspace , computer science , data set , data mining , set (abstract data type) , pattern recognition (psychology) , clustering high dimensional data , artificial intelligence , mathematics , cluster analysis , geometry , programming language
In high dimensional data large no of outliers are embedded in low dimensional subspaces known as projected outliers, but most of existing outlier detection techniques are unable to find these projected outliers, because these methods perform detection of abnormal patterns in full data space. So, outlier detection in high dimensional data becomes an important research problem. In this paper we are proposing an approach for outlier detection of high dimensional data. Here we are modifying the existing SPOT approach by adding three new concepts namely Adaption of Sparse Sub-Space Template (SST), Different combination of PCS parameters and set of non outlying cells for testing data set.