The week's pick
Random Articles
Reseach Article
Intelligent Clustering Engine Solution for Desktop Usability
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 148 - Number 13 |
| Year of Publication: 2016 |
| Authors: Prajakta Pawar, Sushopti Gawade, Sharvari Govilkar |
10.5120/ijca2016911086
|
Prajakta Pawar, Sushopti Gawade, Sharvari Govilkar . Intelligent Clustering Engine Solution for Desktop Usability. International Journal of Computer Applications. 148, 13 ( Aug 2016), 7-11. DOI=10.5120/ijca2016911086
Abstract
ICE stands for Intelligent Clustering Engine. Term weighted similarity measure algorithm, phrase matching algorithm and Document Index Graph Based Clustering algorithm are the algorithms of the project ‘Intelligent Clustering Engine Solution for Desktop Usability”. The Engine is based on SAGH methodology. SAGH stands for Genetic Analytical System for Grouping Hypertext. The stages in SAGH are pre-processing, matrix formation, vectors, clustering, visualization, and output. Visualization enhancement is one of the Usability characteristic that has been enhanced within the proposed system Intelligent Clustering Engine. The paper focuses upon implementing the document clustering application over desktop to improvise usability.
References
- Intelligent Clustering Engine: A clustering gadget for Google Desktop, Authors- Carlantonio, Osiek, Xerox, Costa, Journal- ESWA, 2012
- SRCluster Web Clustering Engine based on Wikipedia, Authors-Meiyappan, Iyengar and Kannan, Journal- International Journal of Advanced Science and Technology, Vol. 39, February, 2012
- A Survey of Web Clustering Engines, Authors- Carpineto, Stanislaw, Romano, Weiss, Journal- ACM Computing Surveys, Vol. 41, No. 3, Article 17, July 2009
- Carrot2 Clustering Framework, Authors- Weiss, Poland and Osi´nski, Journal-EKMA, July 2005
- A Document Clustering Approach for Search Engines, Authors- Liang, Huei Ho, Yang and Chiang, Journal- IEEE International Conference on Systems, Man, and Cybernetics October 2006
- An Efficient Algorithm for Clustering Search Engine Results, Authors-Zhang, Pang, Xie, and Wu, IEEE 2006
- Link Based K-Means Clustering Algorithm for Information Retrieval, Authors- Sathya, Jayanthi, Basker, TN, India, Journal - IEEE, 2011
- Web Document Clustering Using Document Index Graph, Authors- Momin, Kulkarni, Chaudhari, Journal- IEEE, 2011
- An improved measuring similarity for short text snippets and its application in Clustering Search Engine, Authors- Li, Peng, Peng, Jia, Journal- Seventh International Conference on Machine Learning and Cybernetics, Kunming, July 2008
- A Survey of text clustering algorithms , Authors- Aggarwal, Zhai, Watson Research Center, University of Illinois, Journal- IEEE, 1984- 2012
- Similarity Measures for Text Document Clustering, Author-Anna Huang, Department of Computer Science, University of Waikato, 1998- 2007
- Document Clustering with Semantic Analysis, Authors- Wang, Julia Hodges, Department of Computer Science & Engineering, Journal - 39th Hawaii International Conference on System Sciences, 2006
- A Comparison of Document Clustering Techniques, Authors- Steinbach, Karypis, Kumar, Department of Computer Science and Engineering, University of Minnesota
- Website1: Aduna–AutoFocus, Link- http://www.aduna-software.com/technologies/autofocus/overview.view,Visited in July, 2009, Carrot2 Search (2011). Website2: Carrot2 clustering engine, link- http://search.Carrot2 .org/stable/search, visited September 2011
- Website3: http://nlp.stanford.edu/IR-book/html/htmledition/single-link-and-complete-link-clustering-1.html Single Linkage clustering, Complete Linkage Clustering
Index Terms
Keywords