CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Survey on Efficient Clustering Methods with Effective Pruning Techniques for Probabilistic Graphs

by M.balaganesh, G.bharathikannan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 6
Year of Publication: 2015
Authors: M.balaganesh, G.bharathikannan
10.5120/19979-0721

M.balaganesh, G.bharathikannan . A Survey on Efficient Clustering Methods with Effective Pruning Techniques for Probabilistic Graphs. International Journal of Computer Applications. 114, 6 ( March 2015), 1-3. DOI=10.5120/19979-0721

@article{ 10.5120/19979-0721,
author = { M.balaganesh, G.bharathikannan },
title = { A Survey on Efficient Clustering Methods with Effective Pruning Techniques for Probabilistic Graphs },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 6 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number6/19979-0721/ },
doi = { 10.5120/19979-0721 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:51:57.433446+05:30
%A M.balaganesh
%A G.bharathikannan
%T A Survey on Efficient Clustering Methods with Effective Pruning Techniques for Probabilistic Graphs
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 6
%P 1-3
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper provides a survey on K-NN queries, DCR query, agglomerative complete linkage clustering and Extension of edit-distance-based definition graph algorithm and solving decision problems under uncertainty. This existing system give an beginning to Graph agglomeration aims to divide information into clusters per their similarities, and variety of algorithms are planned for agglomeration graphs, the pKwik Cluster algorithm, spectral agglomeration, k-path agglomeration, etc. However, very little analysis has been performed to develop efficient agglomeration algorithms for probabilistic graphs. Finally, The Graph algorithm to understand how to mining can be done efficiently. This survey introduced to design algorithm for searching and to evaluate the algorithm throw analysis.

References
  1. Bonchi. F, Gionis. A, Kollios. G and Potamias. M, PVLDB, vol. 3, no. 1, pp. 997–1008, Sept. 2010. "K-nearest neighbors in uncertain graphs,"
  2. Ding. B, Jin. R, Liu. L and Wang. H, PVLDB, vol. 4, no. 9, pp. 551–562, Jun. 2011. "Distance-constraint reachability computation in uncertain graphs,"
  3. Chen. L, Wang. G, Wang. H and Yuan. Y, PVLDB, vol. 5, no. 9, pp. 800–811, May 2012. "Efficient subgraph similarity search on large probabilistic graph databases,"
  4. Hua. M and Pei. J, in Proc. 13th Int. EDBT, New York, NY, USA, 2010, pp. 347–358. "Probabilistic path queries in road networks: Traffic uncertainty aware path selection,"
  5. Flynn. P. J, Jain. A. K and Murty. M. N, ACM Comput. Surv. vol. 31, no. 3, pp. 264–323,Sept. 1999 "Data clustering: A review,"
  6. Shamir. R, Sharan. R, and Tsur. D, Discrete Appl. Math. , vol. 144, no. 1–2,pp. 173–182, 2004. "Cluster graph modification problems,"
  7. Cetindil. I, Esmaelnezhad. J, Li. C, and Newman. D, in WebDB, 2012, pp. 7–12. "Analysis of instant search query logs,"
  9. Miller. R. B, in Proceedings of the December 9-11, 1968, fall joint computer conference, part I, ser. AFIPS '68 (Fall, part I). New York, NY, USA: ACM, 1968, pp. 267–277. "Response time in man-computer conversational transactions,"
  10. Henzinger. M. R, Marais. H, Moricz. M and Silverstein. C, "Analysis of a very large web search engine query log,"
  11. Ackermann. M. R, Blömer. J, Kuntze. D, and Sohler. C, Algorithmica, vol. 69, no. 1, pp. 184–215, May 2014. "Analysis of agglomerative clustering,"
  12. Broschart. A, Schenkel. R, Theobald. M, won Hwang. S and Weikum. G, in SPIRE, 2007, pp. 287– 299. "Efficient text proximity search,"
  13. Shi. S, Suel. T, Wen. J. R, Yan. H and Zhang. F. in CIKM, 2010, pp. 1229– 1238. "Efficient term proximity search with term-pair indexes,"
  14. Shi. S, Wen. J. R, Yu. N and Zhu. M. in CIKM, 2008, pp. 679–688. "Can phrase indexing help to process non-phrase queries?"
  15. Jain. A and Pennacchiotti. M, in COLING, 2010, pp. 510–518. "Open entity extraction from web search query logs,"
  16. Grabski. K and Scheffer. T, in SIGIR, 2004, pp. 433–439. "Sentence completion,"
Index Terms

Computer Science
Information Sciences

Keywords

Cluster Probabilistic Graphs pKwik Cluster algorithm

Powered by PhDFocusTM