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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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

id	sg-smu-ink.sis_research-7286
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spelling	sg-smu-ink.sis_research-72862021-11-23T07:57:29Z Topic detection, tracking, and trend analysis using self-organizing neural networks RAJARAMAN, Kanagasabai TAN, Ah-hwee 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. 2001-04-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6283 https://ink.library.smu.edu.sg/context/sis_research/article/7286/viewcontent/trac_pakdd01.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University topic detection topic tracking trend analysis text mining document clustering Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	topic detection topic tracking trend analysis text mining document clustering Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing
spellingShingle	topic detection topic tracking trend analysis text mining document clustering Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing RAJARAMAN, Kanagasabai TAN, Ah-hwee Topic detection, tracking, and trend analysis using self-organizing neural networks
description	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.
format	text
author	RAJARAMAN, Kanagasabai TAN, Ah-hwee
author_facet	RAJARAMAN, Kanagasabai TAN, Ah-hwee
author_sort	RAJARAMAN, Kanagasabai
title	Topic detection, tracking, and trend analysis using self-organizing neural networks
title_short	Topic detection, tracking, and trend analysis using self-organizing neural networks
title_full	Topic detection, tracking, and trend analysis using self-organizing neural networks
title_fullStr	Topic detection, tracking, and trend analysis using self-organizing neural networks
title_full_unstemmed	Topic detection, tracking, and trend analysis using self-organizing neural networks
title_sort	topic detection, tracking, and trend analysis using self-organizing neural networks
publisher	Institutional Knowledge at Singapore Management University
publishDate	2001
url	https://ink.library.smu.edu.sg/sis_research/6283 https://ink.library.smu.edu.sg/context/sis_research/article/7286/viewcontent/trac_pakdd01.pdf
_version_	1770575915174068224

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

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