Inverted Index based Modified Version of K-Means Algorithm for Text Clustering

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text clustering, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the k means algorithm adaptable to string vectors for text clustering.

[1]  G. Celeux,et al.  A Classification EM algorithm for clustering and two stochastic versions , 1992 .

[2]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Félix de Moya Anegón,et al.  Document organization using Kohonen's algorithm , 2002, Inf. Process. Manag..

[4]  Taeho Jo,et al.  The Evaluation Measure of Text Clustering for the Variable Number of Clusters , 2007, ISNN.

[5]  Inderjit S. Dhillon,et al.  Generative model-based clustering of directional data , 2003, KDD '03.

[6]  Timo Honkela,et al.  WEBSOM - Self-organizing maps of document collections , 1998, Neurocomputing.

[7]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[8]  Nello Cristianini,et al.  Classification using String Kernels , 2000 .

[9]  Peter Jackson,et al.  Natural language processing for online applications : text retrieval, extraction and categorization , 2002 .

[10]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[11]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[12]  Luis Gravano,et al.  An investigation of linguistic features and clustering algorithms for topical document clustering , 2000, SIGIR '00.

[13]  Gérard Govaert,et al.  Convergence of an EM-type algorithm for spatial clustering , 1998, Pattern Recognit. Lett..

[14]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.