A Comparative Study of Feature Vector-Based Topic Detection Schemes A Comparative Study of Feature Vector-Based Topic Detection Schemes
| Autor: | Hiroyuki Kitagawa, Jia-Yu Pan, Christos Faloutsos, Masafumi Hamamoto |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Signal processing
business.industry Computer science Feature vector Pattern recognition Subject (documents) computer.software_genre Independent component analysis ComputingMethodologies_PATTERNRECOGNITION Text mining Singular value decomposition Data analysis Artificial intelligence Data mining business Cluster analysis computer |
| Zdroj: | WIRI |
| DOI: | 10.1109/wiri.2005.1 |
| Popis: | Topic detection is an important subject when voluminous text data is sent continuously to a user. We examine a method to detect topics in text data using feature vectors. Feature vectors represent the main distribution of data and they are obtained by various data analysis methods. This paper examines three methods: singular value decomposition (SVD), clustering, and independent component analysis (ICA). SVD and clustering are popular existing methods. Clustering, especially, is applied to many topic detection methods. ICA was recently developed in signal processing research. In applications related to text data, however, ICA has not been compared with SVD and clustering, nor has its relationship with them been explored. This paper reports comparative experiments for these three methods and then shows properties as they apply to text data |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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