ugc approved journal list IJRTI Research Journal
International Journal for Research Trends and Innovation
An International Open Access Journal | UGC and ISSN Approved
Impact Factor: 4.87

Call For Paper

Issue: July 2018

Volume 3 | Issue 7

Impact Factor: 4.87

Submit Paper Online

Click Here For more Details

For Authors

Forms / Download

Editorial Board

Subscribe IJRTI

Facts & Figure

Impact Factor : 4.87

Issue per Year : 12

Volume Published : 3

Issue Published : 26

Article Submitted : 1002

Article Published : 617

Total Authors : 1712

Total Reviewer : 500

Total Pages : 188

Total Countries : 10

Visitor Counter


Indexing Partner

Published Paper Details
Paper Title: An Competent Artificial Bee Colony (ABC) and Fuzzy C Means Clustering Using Neuro-Fuzzy Discriminant Analysis from Gene Expression Data
Authors Name: Sathishkumar , Dr.V.Thiagarasu , Dr.E.Balamurugan , Dr.M.Ramalingam
Unique Id: IJRTI1804005
Published In: Volume 3 Issue 4, April-2018
Abstract: This paper looks at the gene microarray data based on the pattern of gene expression using various clustering algorithms. To overcome the problems in gene expression analysis which are generally overlooked by the out-dated clustering algorithms we propose a novel algorithms for finding the co-regulated clusters, dimensionality reduction and clustering. The co-regulated clusters are determined using bi-correlation clustering algorithm (BCCA), also known as co-regulated biclusters. BCCA partakes erected bright to fruitage a numerous settled of biclusters of co-regulated genes over a slice of samples where all the genes in a bicluster have a similar alteration. The dimensionality diminution of microarray gene appearance data is carried out using Neuro - Fuzzy Discriminant Analysis (NFDA). To endure pledge amid the localities in area, NFDA is used and a well-organized Meta experiential optimization algorithm called Artificial Bee Colony (ABC) using Fuzzy C Means clustering is used for clustering the gene expression built on the strategy. The investigational results show that the proposed algorithm achieved a higher clustering accurateness and takes less clustering time when associated with existing algorithms.
Keywords: Gene expression data, Bimax Algorithm, Neuro- fuzzy Discriminant Analysis, Artificial Bee Colony, Fuzzy C Means, Dimensionality Reduction.
Cite Article: "An Competent Artificial Bee Colony (ABC) and Fuzzy C Means Clustering Using Neuro-Fuzzy Discriminant Analysis from Gene Expression Data", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 4, page no.24 - 28, April-2018, Available :http://www.ijrti.org/papers/IJRTI1804005.pdf
Downloads: 00092
Publication Details: Published Paper ID: IJRTI1804005
Registration ID:180044
Published In: Volume 3 Issue 4, April-2018
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
Share Article:

Click Here to Download This Article

Article Preview



Major Indexing from www.ijrti.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

DOI (A digital object identifier)



Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related

RMS

Conference Proposal

Latest News / Updates

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Untitled Document