From Chaos to Clarity-A New Era in Document Clustering & Classification

DOI

10.2991/978-94-6463-858-5_267How to use a DOI?

Keywords

High-dimensional text; Clustering; Python; K-means; Stopwords; Elbow Curve; Precision; Recall

Abstract

High-dimensional documents play a crucial role in classification tasks, yet their size often raises challenges and signals potential issues. Dimensional reduction, while offering both advantages and disadvantages, becomes pivotal in managing these challenges. However, improper dimensional reduction can hinder achieving optimal outcomes during classification. This work explores how simplified data representations can retain essential features. In this study, we investigated various datasets and applied clustering and classification techniques, such as K-means clustering and Naive Bayes classification, to improve efficiency. By proposing enhancements to these methods, the paper tackles the dual objectives of effective clustering and accurate classification. Metrics like precision, recall, and F-score are employed to evaluate the proposed methodology. Experimental results indicate that the suggested approach delivers superior performance compared to existing algorithms, demonstrating its efficacy in addressing high-dimensional text classification and clustering challenges.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Marry Prabhakar
AU  - K. Sreelekha
AU  - K. Shivani
AU  - V. Gopal
AU  - K. Satya Hemanth Kumar
PY  - 2025
DA  - 2025/11/04
TI  - From Chaos to Clarity-A New Era in Document Clustering & Classification
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3207
EP  - 3217
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_267
DO  - 10.2991/978-94-6463-858-5_267
ID  - Prabhakar2025
ER  -