International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 12, December (2014), pp. 257-263© IAEME 257 OUTLIER DETECTION THROUGH ONLINE OVER SAMPLING CELLUAR AUTOMATA BASED PCA STRENGTHENED WITH CELLULAR AUTOMATA M.Divya Final M Tech Student, Dept of Computer Science and Engineering, Sri Vishnu Engineering College for Women, Bhimavaram Pokkuluri Kiran Sree Associate Professor, Dept of Computer Science and Engineering, Sri Vishnu Engineering College for Women, Bhimavaram ABSTRACT Abnormality identification has been an essential exploration subject in information mining and machine learning. A lot of people true applications, for example, interruption or Mastercard extortion location require a compelling and productive skeleton to distinguish strayed information cases. Then again, most inconsistency recognition systems are commonly executed in clump mode, and therefore can't be effectively stretched out to substantial scale issues without giving up processing and memory prerequisites. In this paper, we propose an online over-inspecting important segment investigation calculation to address this issue, and we go for distinguishing the vicinity of outliers from a lot of information by means of a web overhauling procedure. Dissimilar to earlier CELLUAR AUTOMATA BASED PCA based methodologies, we don't store the whole information framework or covariance grid, and therefore our methodology is particularly of enthusiasm toward online or extensive scale issues. By over-examining the target occasion and concentrating the primary heading of the information, the proposed osCelluar Automata Based PCA permits us to focus the irregularity of the target example as per the variety of the ensuing overwhelming eigenvector. Since our osCelluar Automata Based PCA require not perform eigen investigation unequivocally, the proposed skeleton is favored for online applications which have calculation or memory limits. Contrasted and the well known power system for CELLUAR AUTOMATA BASED PCA and other well known inconsistency recognition calculations, our exploratory results check the practicality of our proposed strategy as far as both precision and productivity. We use the cellular automata classifier for strengthening the system. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 12, December (2014), pp. 257-263 © IAEME: www.iaeme.com/IJCET.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E