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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Institutional Knowledge at Singapore Management University
2001
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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 |
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SMU Libraries |
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Asia |
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Singapore Singapore |
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SMU Libraries |
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InK@SMU |
| language |
English |
| topic |
topic detection topic tracking trend analysis text mining document clustering Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing |
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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 |