SPMETS: Sequential Pattern Mining in Exceptional Text Streams using WEKA Tool

Authors

  • U Saranya Dept. of Computer Science, Marudupandiyar College of Arts and Science, Thanjavur, India
  • S Padmavathi Dept. of Computer Science, Marudupandiyar College of Arts and Science, Thanjavur, India

DOI:

https://doi.org/10.26438/ijcse/v5i7.2023

Keywords:

Document Streams, Dynamic Programming, Pattern-Growth, Rare Event, Sequential Patterns, Web Mining

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.

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Published

2025-11-11
CITATION
DOI: 10.26438/ijcse/v5i7.2023
Published: 2025-11-11

How to Cite

[1]
U. Saranya and S. Padmavathi, “SPMETS: Sequential Pattern Mining in Exceptional Text Streams using WEKA Tool”, Int. J. Comp. Sci. Eng., vol. 5, no. 7, pp. 20–23, Nov. 2025.

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Section

Review Article