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SPMETS: Sequential Pattern Mining in Exceptional Text Streams using WEKA Tool
SPMETS: Sequential Pattern Mining in Exceptional Text Streams using WEKA Tool
U. Saranya1 , S. Padmavathi2
1 Dept. of Computer Science, Marudupandiyar College of Arts and Science, Thanjavur, India.
2 Dept. of Computer Science, Marudupandiyar College of Arts and Science, Thanjavur, India.
Correspondence should be addressed to: balasaranya47@gmail.com .

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 20-23, Jul-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i7.2023

Online published on Jul 30, 2017

Copyright © U. Saranya, S. Padmavathi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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Citation

IEEE Style Citation: U. Saranya, S. Padmavathi, “SPMETS: Sequential Pattern Mining in Exceptional Text Streams using WEKA Tool”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.20-23, 2017.

MLA Style Citation: U. Saranya, S. Padmavathi "SPMETS: Sequential Pattern Mining in Exceptional Text Streams using WEKA Tool." International Journal of Computer Sciences and Engineering 5.7 (2017): 20-23.

APA Style Citation: U. Saranya, S. Padmavathi, (2017). SPMETS: Sequential Pattern Mining in Exceptional Text Streams using WEKA Tool. International Journal of Computer Sciences and Engineering, 5(7), 20-23.
           
Abstract :
Checking and making sense of the rich and continuously refreshed document in an online medium can yield important data that allows users and association increase useful Information about progressing events and consequently make quick move. This calls for powerful ways to precisely screen break down and summarize the Important data present in an on the web. Customarily term-based and word-based approaches used for data sifting. Theme demonstrate has used for discovering unseen topics in a set of qualification. Term-based and Word-based approaches have disadvantage which are polysemous and synonymy. The animal of propensity mining procedure used in field of theme demonstrating generates show for discovering more significant and discriminative topics from accumulation of documents.
Key-Words / Index Term :
Document Streams, Dynamic Programming, Pattern-Growth, Rare Event, Sequential Patterns, Web Mining
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