Topic detection, tracking, and trend analysis using self-organizing neural networks

Author

Kanagasabai RAJARAMAN
Ah-hwee TAN, Singapore Management UniversityFollow

Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2001

Abstract

We address the problem of Topic Detection and Tracking (TDT) and subsequently detecting trends from a stream of text documents. Formulating TDT as a clustering problem in a class of self-organizing neural networks, we propose an incremental clustering algorithm. On this setup we show how trends can be identified. Through experimental studies, we observe that our method enables discovering interesting trends that are deducible only from reading all relevant documents.

Keywords

topic detection, topic tracking, trend analysis, text mining, document clustering

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings, Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01), LNAI 2035

Volume

2035

City or Country

Hong Kong, China

Citation

1

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.