CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

A Review on Clustering Method based on Unsupervised Learning Approach

by Neeraj Sharma, Priyanka Sharma, Kretika Tiwari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 19
Year of Publication: 2018
Authors: Neeraj Sharma, Priyanka Sharma, Kretika Tiwari
10.5120/ijca2018917878

Neeraj Sharma, Priyanka Sharma, Kretika Tiwari . A Review on Clustering Method based on Unsupervised Learning Approach. International Journal of Computer Applications. 181, 19 ( Sep 2018), 20-23. DOI=10.5120/ijca2018917878

@article{ 10.5120/ijca2018917878,
author = { Neeraj Sharma, Priyanka Sharma, Kretika Tiwari },
title = { A Review on Clustering Method based on Unsupervised Learning Approach },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 19 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 20-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number19/29972-2018917878/ },
doi = { 10.5120/ijca2018917878 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:25.140786+05:30
%A Neeraj Sharma
%A Priyanka Sharma
%A Kretika Tiwari
%T A Review on Clustering Method based on Unsupervised Learning Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 19
%P 20-23
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining main goal of information find in large dataset or the data mining process is to take out information from an outsized data set and transform it into a clear kind for any use. group is vital in information analysis and data processing applications. it's the task of clustering a group of objects in order that objects within the same group are additional kind of like different or one another than to those in other teams (clusters).speedy recovery of the related data from databases has invariably been a big issue. There are several techniques are developed for this purpose; in among information cluster is one amongst the key techniques. The method of making very important data from a large quantity of information is learning. It may be classified into 2 like supervised learning and unsupervised learning. Group could be a quite unsupervised data processing technique. It describes the overall operating behavior, the methodologies followed by these approaches and therefore the parameters that have an effect on the performance of those algorithms. a review of cluster and its completely different techniques in data processing is completed.

References
  1. A. Jain, M. Murty, and P. Flynn, “Data clustering: A review,” ACM Comput. Surv., vol. 31, no. 3, pp. 264–323, 1999.
  2. Jiawei Han, Micheline Kamber, “Data Mining Concepts and Techniques” Elsevier Publication.
  3. Pavel Berkhin, “A Survey of Clustering Data Mining Techniques”, pp.25-71, 2002.
  4. Cheng-Ru Lin, Chen, Ming-Syan Syan , “Combining Partitional and Hierarchical Algorithms for Robust and Efficient Data Clustering with Cohesion Self-Merging” IEEE Transactions On Knowledge And Data Engineering, Vol. 17, No. 2,pp.145-159, 2005.
  5. A. Geva, “Hierarchical unsupervised fuzzy clustering,” IEEE Trans. Fuzzy Syst., vol. 7, no. 6, pp. 723–733, Dec. 1999.
  6. Y. S. Thakare, S. B. Bagal, .Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics., International Journal of Computer Applications (0975 . 8887) Volume 110 . No. 11, January 2015.
  7. R. Hammah and J. Curran, “Validity measures for the fuzzy cluster analysis of orientations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1467–1472, Dec. 2000.
  8. Ankita Vimal, Satyanarayana R Valluri, Kamalakar Karlapalem (2008) “An Experiment with Distance Measures for Clustering” International Conference on Management of Data COMAD 2008,pp.241-244.
  9. Navjot Kaur, J K Sahiwal, Navneet Kaur “Efficient Kmeans clustering Algorithm Using Ranking Method In Data Mining”, ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May2012.
  10. Juntao Wang & Xiaolong Su, “An improved K-Means clustering algorithm”, IEEE, 2011.
  11. Y. S. Thakare, S. B. Bagal, .Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics.,International Journal of Computer Applications (0975 . 8887)Volume 110 . No. 11, January 2015.
  12. Mariam El-Tarabily, Rehab Abdel-Kader, Mahmoud Marie, Gamal Abdel-Azeem, “A PSO-Based Subtractive Data Clustering Algorithm,” International Journal of Research in Computer Science eISSN 2249-8265 Volume 3 Issue 2 (2013) pp. 1-9.
  13. Soumi Ghosh, Sanjay Kumar Dubey, . Comparative Analysis of K-Means and Fuzzy C-Means Algorithms., International Journal of Advanced Computer Science and Applications, Vol. 4, No.4, 2013
  14. Debashis Sen , Sankar K. Pal “Generalized Rough Sets, Entropy, and Image Ambiguity Measures”, pp. 117-128, 2009.
  15. Dariusz Małyszko, Jarosław Stepaniuk “Adaptive Rough Entropy Clustering Algorithms in Image Segmentation”, pp. 199-2312010.
  16. Ran Vijay Singh, M.P.S Bhatia, .Data Clustering with Modified K-means Algorithm., Recent Trends in Information Technology,n2011 IEEE International Conference on 3-5 June 2011(pp. 717- 721)
Index Terms

Computer Science
Information Sciences

Keywords

Clustering unsupervised learning FCM KMC HC.