Proceeding of

NCAICN National Conference 2013



Advances in

Computing & Networking


A Special Issue of

International Journal of Computer Science and Applications



Hon. Shri Sundeepji Meghe

(Chairman, Vidarbha Youth Welfare Society, Amravati)



Dr. V.T. Ingole (FIE, FIETE, Professor Emeritus)


Organizing Committee


Dr. D.T. Ingole (FIE, FIETE)

(Principal PRMIT & R, Badnera and  Chairman IEI  Amravati Center).


 Er. A.W. Jawanjal

(Honorary Secretary IEI, Amravati Center)


Dr. G.R. Bamnote ((FIE, FIETE)

(H.O.D. Computer Science & Engineering)

Dr. A.S. Alvi (MIE)

(H.O.D. Information Technology))

Prof. Mrs. M.D. Ingole (FIE.MIETE)

(H.O.D. Electronics & Telecommunication)


Prof. S.V. Dhopte ((FIE, FIETE)

Prof. Ms. V.M. Deshmukh (FIE, FIETE)

Dr. S.W. Mohod  (FIE,FIETE)


Dr. S.R. Gupta (MIE, MIETE)

Prof. S.V. Pattalwar ((FIE, FIETE)

Prof. M.D. Damahe


Prof. Mrs. M.S. Joshi                

Dr. S.M. Deshmukh

Prof. V.U. Kale

Prof. S.S. Kulkarni

Prof. Ms. R.R. Tuteja

Prof. Ms. J.N. Ingole

Prof. V.R. Raut

Prof. C.N. Deshmukh

Prof. Ms. M.S. Deshmukh

Prof. S.P. Akarte

Prof. Mrs. A.P. Deshmukh

Prof. Mrs. S.S. Sikchi

Prof. N.N. Khalsa

Department of Information and Computer Science and Engineering

Prof. Ram Meghe Institute of Technology and Research, Badnera Distt. Amravati



Prof. K. H. Walse





IJCSA ISSN: 0974-1011 (Online) >>    

Joint Approach for Outlier Detection

Niketa V. Kadam and Prof. M. A. Pund


Outlier is nothing but the unusual result in the dataset. Now a days outlier detection becomes the need. The serious problem arises with segmenting the data into the number of chunks. To improve the effectiveness of outlier detection, we need proper partition of the data. For the detection of the outlier first the data must be segmented into the number of chunks and after that each chunk is compared with the another one for getting the candidate outlier From the computational point of view, the main obstacle to find the outlier is the vast dataset. In realistic settings, all of the above complicating factors do not appear in isolation, but contribute collectively to increasing the complexity of the comparison problem.

There are the different logical approaches to detect outlier data values, such as clustering algorithms like, K-mean or K-median, which can produce good clustering results and at the same time deserves good scalability. Finally, distance based technique is used to find the distance from centroid to candidate outlier. So that, both approaches take less computational cost. In brief, to avoid pair wise distance calculations, to detect better outlier even if the evolution of data set change, to let user free to provide sensitive parameters, and to mine Data set even in limited memory resources here we propose a clustering based method because; the clustering methods have good space and time complexity.

2013 International Journal of Computer Science and Applications 

Published by Research Publications, India