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Reseach Article

PICGraph: An Extension of Power Iteration Clustering for Inferring Conceptual Relationships from Large Unstructured Datasets

Published on September 2015 by Anoop V.s., Lekshmi B.g
International Conference on Emerging Trends in Technology and Applied Sciences
Foundation of Computer Science USA
ICETTAS2015 - Number 3
September 2015
Authors: Anoop V.s., Lekshmi B.g
f4c57bd6-86ad-4619-8bbe-d0de457f85fc

Anoop V.s., Lekshmi B.g . PICGraph: An Extension of Power Iteration Clustering for Inferring Conceptual Relationships from Large Unstructured Datasets. International Conference on Emerging Trends in Technology and Applied Sciences. ICETTAS2015, 3 (September 2015), 26-30.

@article{
author = { Anoop V.s., Lekshmi B.g },
title = { PICGraph: An Extension of Power Iteration Clustering for Inferring Conceptual Relationships from Large Unstructured Datasets },
journal = { International Conference on Emerging Trends in Technology and Applied Sciences },
issue_date = { September 2015 },
volume = { ICETTAS2015 },
number = { 3 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 26-30 },
numpages = 5,
url = { /proceedings/icettas2015/number3/22391-2594/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emerging Trends in Technology and Applied Sciences
%A Anoop V.s.
%A Lekshmi B.g
%T PICGraph: An Extension of Power Iteration Clustering for Inferring Conceptual Relationships from Large Unstructured Datasets
%J International Conference on Emerging Trends in Technology and Applied Sciences
%@ 0975-8887
%V ICETTAS2015
%N 3
%P 26-30
%D 2015
%I International Journal of Computer Applications
Abstract

Data mining and Information extraction is an emerging research area that attempts to find out the hidden and relevant knowledge from the overwhelming amount of information available in structured, semi structured and unstructured forms. Power Iteration Clustering is a powerful clustering algorithm that is proved to be efficient for the clustering of structured data. Large amount of data available in Gmail, micro-blogs etc doesn't have proper structure renowned as unstructured data which covers remarkable percentage of data available on the internet. Due to the unordered structure of the data, the extraction of relevant information from this huge collection is a complex task. This work used the predominant features of Power Iteration Clustering algorithm for the extraction of information and visualization of unstructured data from micro blogging sites.

References
  1. Nahm, Un Yong, and Raymond J. Mooney. Text mining with information extraction. AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases. Vol. 1. 2002.
  2. Karanikas, Haralampos, Christos Tjortjis, and Babis Theodoulidis. An approach to text mining using information extraction. Proc. Knowledge Management Theory Applications orkshop,(KMTA). 2000.
  3. Lin, Frank, and William W. Cohen. Power iteration clustering. In Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010.
  4. Dalvi, Bhavana, and W. Cohen. Very fast similarity queries on semi structured data from the web. SDM. 2013. K. Elissa
  5. Toshniwal, Durga, and Rishiraj Saha Roy. Clustering Unstructured Text Documents Using Naive Bayesian Concept and Shape Pattern Matching. IJACT: International Journal of Advancements in Computing Technology. 2009.
  6. Lu, Yen-ling, and Chin-Shyurng Fahn. Hierarchical artificial neural networks for recognizing high similar large data sets. International Conference on Machine Learning and Cybernetics. IEEE, 2007. Archana Singh, Megha Chaudhary, Dr (Prof. ) Ajay Rana, Gaurav Dubey, Online Mining of data to Generate Association Rule Mining in Large Databases , International Conference on Recent Trends in Information Systems. 2011
  7. David N. Reshef et al. ,Detecting Novel Associations in Large Data Sets. Science AAAS. 2011
  8. L. Xinwu. Research on Text Clustering Algorithm Based on k-means and SOM. International Symposium on Intelligent Information Technology Application Workshops 2008
  9. X. Liu, P. He, H. Wang. The Research of Text Clustering Algorithms Based on Frequent Term Sets. Proc. International Conference on Machine Learning and Cybernetics 2005
  10. Fiedler, Miroslav. An Algebraic Approach to Connectivity off Graphs. Recent advances in graph theory: proceedings of the Symposium held in Prague, June 1975
  11. Schaeffer, Satu Elisa. "Graph clustering. " Computer Science Review 1. 1 (2007): 27-64.
  12. Xu, Rui, and Donald Wunsch. "Survey of clustering algorithms. " Neural Networks, IEEE Transactions on 16. 3 (2005): 645-678.
  13. Dhillon, Inderjit S. "Co-clustering documents and words using bipartite spectral graph partitioning. " Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001.
  14. Hartuv, Erez, and Ron Shamir. "A clustering algorithm based on graph connectivity. " Information processing letters 76. 4 (2000): 175-181.
Index Terms

Computer Science
Information Sciences

Keywords

Information Extraction Micro Blogs Power Iteration Clustering Unstructured Data