Abstract
Due to the high-dimension and sparseness properties of documents, clustering the similar documents together is a tough task. The most popular document clustering method K-Means has the shortcoming of its cluster intra-dissimilarity, i.e. inclining to clustering unrelated documents together. One of the reasons is that all objects (documents) in a cluster produce the same influence to the mean of the cluster. SOM (Self Organizing Map) is a method to reduce the dimension of data and display the data in low dimension space, and it has been applied successfully to clustering of high-dimensional objects. The scalar factor is an important part of SOM. In this paper, an optimized K-Means algorithm is proposed. It introduces the scalar factor from SOM into means during K-Means assignment stage for controlling the influence to the means from new objects. Experiments show that the optimized K-Means algorithm has more F-Measure and less Entropy of clustering than standard K-Means algorithm, thereby reduces the intra-dissimilarity of clusters effectively.
This work is supported by National Natural Science Foundation of China (No. 60173051)
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Wang, D., Yu, G., Bao, Y., Zhang, M. (2005). An Optimized K-Means Algorithm of Reducing Cluster Intra-dissimilarity for Document Clustering. In: Fan, W., Wu, Z., Yang, J. (eds) Advances in Web-Age Information Management. WAIM 2005. Lecture Notes in Computer Science, vol 3739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563952_81
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DOI: https://doi.org/10.1007/11563952_81
Publisher Name: Springer, Berlin, Heidelberg
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