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| A Text Clustering Method Based on Speech to Text and Improved Center Selection |
| SHI Kan-Sheng, LIU Hai-Tao, SONG Wen-Tao |
| School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University,Shanghai 200040 |
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Abstract The traditional k-means algorithm is sensitive to the initial point and easy to fall into local optimum. An improved speech to text and improved center selection (STICS) based text clustering method is proposed. Taking into account the speech to text, the optimal selection of centers and treatment of outliers concurrently, STICS has three aspects of improvement. The weighted vector space model (VSM) is used to represent text according to the speech to text. For the selection of the center, the sample average similarity is measured for each sample, and the sample with the largest sample average similarity is selected as the first center. In addition, STICS method eliminates the negative influences of isolated points or outliers. Both theoretical analysis and experimental results prove that the proposed algorithm has better clustering results.
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Received: 25 August 2011
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2012, Vol. 25 
