Effectual Motive Innovation of Text in the Data set Using Text mining

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2015 by IJPTT Journal
	Volume - 5 Issue - 5
	Year of Publication : 2015
	Authors : N.Bhuvaneswari, Dr.N.Chandrakala
	DOI :  10.14445/22492615/IJPTT-V21P402

Citation

N.Bhuvaneswari, Dr.N.Chandrakala "Effectual Motive Innovation of Text in the Data set Using Text mining". International Journal of P2P Network Trends and Technology (IJPTT), V5(5):13-15 Sep - Oct 2015, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.

Abstract

In the effectual motive innovation of the text is using to retrieve the data from the largedata set using text mining. The data mustto characterisedthe text using text mining. In the large scale industries and educational institutes are identify the domain of the area is difficult to search and order the area. In this project used to order the area for easy retrieving. The existing characterised method is identification of the word and characterised the word inaccurate.So the proposed system is based on retrieving text using text mining.In this projects using four steps in the selection work. The proposed system easily identify the particular area comes under the project.

References

1. Jian Ma, Wei Xu, Yong-hong Sun, Efraim Turban, ShouyangWang”An Ontology-Based Text- Mining Method to Cluster Proposals for Research Project Selection”,IEEE Trans an systems and humans vol.42,no.3 May2012
2. Xin Xia; Lo, D.; WeiweiQiu; Xingen Wang; BoZhou “Automated Configuration Bug Report Prediction Using Text Mining” Computer Software and Applications Conference (COMPSAC), 2014 IEEE 38th AnnualYear: 2014
3. Wu Maowen; Zhang Cai Dong; LanWeiyao; Wu Qing Qiang “Text topic mining based on LDA and co-occurrence theory”, Computer Science & Education (ICCSE), 2012 7th International Conference onYear: 2012
4. Preethi, T.; Lakshmi, R. “An implementation of clustering project proposals on ontology based textmining approach”,Information Communication and Embedded Systems (ICICES), 2013 International Conference on Year: 2013
5. 5.Zurada, J.M.; Ensari, T.; Asl, E.H.; Chorowski, J.”Nonnegative Matrix Factorization and its application to pattern analysis and text mining”Computer Science and Information Systems (FedCSIS), 2013 Federated Conference onYear: 2013
6. 6.Hoai Nam Vu; Tuan Anh Tran; In Seop Na; SooHyung Kim “Automatic extraction of text regions from document images by multilevel thresholding and k-means clustering” Computer and Information Science (ICIS), 2015 IEEE/ACIS 14th International Conference on Year: 2015Pages: 329 - 334, DOI: 10.1109/ICIS.2015.7166615IEEE Conference Publications
7. 7. Djellali, C. “Enhancing text clustering model based on Truncated Singular Value Decomposition, fuzzy ART and Cross Validation” Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on Year: 2013 Pages: 1078 – 1083IEEE Conference Publications
8. Agnihotri, D.; Verma, K.; Tripathi, P. “Pattern and Cluster Mining on Text Data “,Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference onYear: 2014Pages: 428 - 432, DOI: 10.1109/CSNT.2014.92IEEE Conference Publications
9. Fonda, W.; Purwarianti, A. “Experiments on keyword list generation by term distribution clustering for text classification”,Advanced Computer Science and Information Systems (ICACSIS), 2014 International Conference on Year: 2014Pages: 297 - 301, DOI: 10.1109/ICACSIS.2014.7065879IEEE Conference Publications
10. Skabar, A.; Abdalgader, K. “Clustering Sentence- Level Text Usinga Novel Fuzzy Relational Clustering Algorithm “Knowledge and Data Engineering, IEEE Transactions on Year: 2013, Volume: 25, Issue: 1 Pages: 62 - 75, DOI: 10.1109/TKDE.2011.205 Cited by: Papers (3)IEEE Journals & Magazines.
11. V.Vijayadeepa,N.Gomathi, “Data Sharing in the Cloud Computing Security Using JAR” IJPTT-V3i6P103.

Keywords
innovation of characterised text, text mining.