Periodicity: Bi Annual.
Impact Factor:
SJIF:5.079 & GIF:0.416
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

Paper Template
Copyright Form
Subscription Form
web counter
web counter

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJCOA provides online manuscript tracking system.

Every issue of Journal of IJCOA is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJCOA special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJCOA website. For complete procedure, contact us at

SCIA Journal Metrics

Published in:   Vol. 9 Issue 1 Date of Publication:   June 2020
Page(s):   26-31 Publisher:   Integrated Intelligent Research (IIR)
DOI:   10.20894/IJCOA. SAI :

Feature selection is one of the recent techniques in data mining. Clustering is an unsupervised learning method in knowledge discovery world. Dimensionality reduction poses electing the significant data from the wider variety of data elements. “Curse of dimensionality “is a challenging issue of present researches which produces wrong outcomes during clustering process. In this paper, cluster based outlier detection method is applied with various multivariate datasets. Before clustering the datasets, the feature selection method has been implemented for selecting significant datasets from the entire training attributes. Feature selection plays an essential role in cluster accuracy for obtaining the dissimilar and dissimilar datasets among the data instances. In this research work, the proposed system is compared with the various correlation based feature selection algorithms and their experimental results are depicted.