Abstract
Feature weighting plays an important role in text clustering. Traditional feature weighting is determined by the syntactic relationship between feature and document (e.g. TF-IDF). In this paper, a semantically enriched feature weighting approach is proposed by introducing the semantic relationship between feature and document, which is implemented by taking account of the local feature relatedness — the relatedness between feature and its contextual features within each individual document. Feature relatedness is measured by two methods, document collection-based implicit relatedness measure and Wikipedia link-based explicit relatedness measure. Experimental results on benchmark data sets show that the new feature weighting approach surpasses traditional syntactic feature weighting. Moreover, clustering quality can be further improved by linearly combining the syntactic and semantic factors. The new feature weighting approach is also compared with two existing feature relatedness-based approaches which consider the global feature relatedness (feature relatedness in the entire feature space) and the inter-document feature relatedness (feature relatedness between different documents) respectively. In the experiments, the new feature weighting approach outperforms these two related work in clustering quality and costs much less computational complexity.
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References
Lan, M., Tan, C., Su, J.: Supervised and traditional term weighting methods for automatic text categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(4), 721–735 (2009)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Jing, L., Zhou, L., Ng, M., Huang, J.: Ontology-based distance measure for text clustering. In: Proc. of the 4th Workshop on Text Mining, the 6th SIAM International Conference on Data Mining (2006)
Wang, P., Domeniconi, C.: Building semantic kernels for text classification using Wikipedia. In: Proc. of the 14th ACM SIGKDD, New York, NY, USA, pp. 713–721 (2008)
Huang, A., Milne, D., Frank, E., Witten, I.: Clustering documents using a wikipedia-based concept representation. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 628–636. Springer, Heidelberg (2009)
Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, Cambridge (2007)
Medelyan, O., Witten, I., Milne, D.: Topic indexing with wikipedia. In: Proc. of the AAAI Wikipedia and AI Workshop (2008)
Wang, P., Hu, J., Zeng, J., Chen, L., Chen, Z.: Improving text classification by using ecyclopedia knowledge. In: Proc. of the 7th ICDM, Omaha, NE, USA, pp. 332–341 (2007)
Huang, A., Milne, D., Frank, E., Witten, I.: Clustering documents with active learning using wikipedia. In: Proc. of International Conference on Data Mining Series, pp. 839–844 (2008)
Hu, X., Zhang, X., Lu, C., Park, E., Zhou, X.: Exploiting wikipedia as external knowledge for document clustering. In: Proc. of the 15th ACM SIGKDD, pp. 389–396 (2009)
Mihalcea, R., Csomai, A.: Wikify! linking documents to encyclopedic knowledge. In: Proc. of the 16th CIKM (2007)
Milne, D., Witten, I.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: Proc. of the Workshop on Wikipedia and Artificial Intelligence at AAAI, pp. 25–30 (2008)
Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 60(5) (2004)
Slonim, N., Tishby, N.: Document clustering using word clusters via the information bottleneck method. In: Proc. of 23th ACM SIGIR, pp. 208–215 (2000)
Steinbach, S., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: Proc. of the Workshop on Text Mining at ACM SIGKDD (2000)
Zhao, Y., Karypis, G.: Comparison of agglomerative and partitional document clustering algorithms. Technical Report 02-014, University of Minnesota (2002)
Zhong, S., Ghosh, J.: A comparative study of generative models for document clustering. In: Proc. of SDW Workshop on Clustering High Dimensional Data and its Applications, San Francisco, CA (2003)
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Yun, J., Jing, L., Yu, J., Huang, H. (2011). Unsupervised Feature Weighting Based on Local Feature Relatedness. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20841-6_4
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DOI: https://doi.org/10.1007/978-3-642-20841-6_4
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