CFP last date
20 January 2025
Reseach Article

Improving Cluster Quality by using Ripley’s K-Function

by Mandeep Kaur, Usvir Kaur, Roop Kamal Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 2
Year of Publication: 2015
Authors: Mandeep Kaur, Usvir Kaur, Roop Kamal Kaur
10.5120/ijca2015905849

Mandeep Kaur, Usvir Kaur, Roop Kamal Kaur . Improving Cluster Quality by using Ripley’s K-Function. International Journal of Computer Applications. 125, 2 ( September 2015), 39-43. DOI=10.5120/ijca2015905849

@article{ 10.5120/ijca2015905849,
author = { Mandeep Kaur, Usvir Kaur, Roop Kamal Kaur },
title = { Improving Cluster Quality by using Ripley’s K-Function },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 2 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number2/22408-2015905849/ },
doi = { 10.5120/ijca2015905849 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:15:00.695007+05:30
%A Mandeep Kaur
%A Usvir Kaur
%A Roop Kamal Kaur
%T Improving Cluster Quality by using Ripley’s K-Function
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 2
%P 39-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As the data on the web is growing rapidly, more and more people rely on the search engines to explore the web .Due to heterogeneous and unstructured nature of the web data, Web mining uses various data mining techniques to extract hidden useful knowledge from Web hyperlinks, page content and web usage logs. Web Usage Mining is one of the applications of data mining techniques that are used to discover interesting usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases: preprocessing, pattern discovery, and pattern analysis. In this paper Ripley’s k-function is used to refine the original clusters obtained by k-mean and weighted k-mean clustering algorithms.

References
  1. Supinder Singh , Sukhpreet Kaur , “ Web Log File Data Clustering Using K-Means and Decision Tree”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 8, August 2013.
  2. Dilpreet kaur, Sukhpreet kaur, “ A Study on User Future Request Prediction Methods Using Web Usage Mining”, International Journal of Computational Engineering Research, Vol 3, Issue 4,April 2013.
  3. Marjan Eshaghi, S.Z. Gawali, “ Web Usage Mining Based on Complex Structure of XML for Web IDS”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) , Volume 2, Issue 5, April 2013.
  4. V.Shanmuga Priya, S.Sakthivel, “An Implementation of Web Personalization using Web Mining Techniques”, International Journal of Computer Science and Mobile Computing Vol.2 Issue. 6, June 2013.
  5. Monika Yadav Mr. Pradeep Mittal, “Web Mining: An Introduction”, International Journal of Advanced Research in Computer Science and Software Engineering,Volume 3, Issue 3, March 2013.
  6. Zhang et al., “An Improved K-means Clustering Algorithm”,Journal of Information & Computational Science, Volume 10, Issue 1 ,2013.
  7. Dr. Mohammed Otair, “APPROXIMATE K-NEAREST NEIGHBOUR BASED SPATIAL CLUSTERING USING K-D TREE”, International Journal of Database Management Systems ( IJDMS ) Vol.5, No.1, February 2013.
  8. Soumi Ghosh, Sanjay Kumar Dubey, “Comparative Analysis of K-Means and Fuzzy C-Means Algorithms”, International Journal of Advanced Computer Science and Application((IJACSA), Vol. 4, No.4, 2013.
  9. Raed T. Aldahdooh, Wesam Ashour, “DIMK-means -Distance-based Initialization Method for K-means Clustering Algorithm”, I.J. Intelligent Systems and Applications, Volume 02, Issue 41-51, January 2013.
  10. Fahim et al., “An efficient enhanced k-means clustering algorithm”, Journal of Zhejiang University SCIENCE A, Volume 7, Issue 10, 2006.
  11. Rostyslav Kosarevych, Bohdan Rusyn, “Application Of The Ripley’s K-Function For Image Segmentation” , TCSET, 2012.
  12. Thibault Lagache, Vannary Meas-Yedid, Jean-Christophe Olivo-Marin, “A Statistical Analysis Of Spatial Colonization Using Ripley’s K-Function”, IEEE 10th International Symposium On Biomedical Imaging:From Nano To Macro, 2013.
  13. Guohui Zhu, Ying Ge,Huachen Wang, “A Modified Ripley’s K-Function To Detecting Spatial Pattern Of Urban System”,
  14. Sampaio, Wener B, Diniz,Edgar M,Silva, Aristofanes C,Paiva, Anselmo C, de, “Detection Of Masses in Mammograms Using Cellular Neural Network, Hidden Markov Models And Ripley’s K-Function”, IEEE, 2009.
  15. Philip M.Dixon , “Ripley’s K-Function”,Volume 3, 2002.
  16. Mandeep kaur, Usvir Kaur, Dr.Dheerendra Singh, “Web log file clustering algorithms: A survey”, International Journal of Computer Application and Technology (IJCAT), Volume 1, April 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Web mining Web Usage mining k-means weighted k-means Ripley’s k-function entropy accuracy precision recall f-measure.