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

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

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...

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Bibliographic Details
Main Authors: RAJARAMAN, Kanagasabai, TAN, Ah-hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2001
Subjects:
topic detection
topic tracking
trend analysis
text mining
document clustering
Artificial Intelligence and Robotics
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/6283
https://ink.library.smu.edu.sg/context/sis_research/article/7286/viewcontent/trac_pakdd01.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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