Abstract
The process of clustering aims to discover natural groupings, and thus present an overview of the classes (topics) in a collection of documents. In the field of artificial intelligence, this is known as unsupervised machine learning. Extraction of internal structure from document collections in the absence of pre-classified training data, is a challenging task in text-mining due to the high dimensionality of the input data (usually in the form of word-frequency vectors derived from the bag-of-words (BOW) model of document representation). Self Organizing Maps (SOM) represents high-dimensional data in the form of topology preserving two-dimensional projections which can be exploited for creating a natural visualization of data and at the same time to accomplish the task of dimensionality reduction. The feature of emergence which is the generation of complex systems and patterns by the cooperation of multiple elementary interactions provides a way of detecting higher level structures or cluster of clusters within a document corpus. The natural visualization of clusters is investigated in this study (rather than classification/categorization) using Emergent Self-Organized Maps by effectively introducing bigrams. Experiments have been conducted using a limited vocabulary of 925 documents containing 2000 unigrams and 1000 bigrams approximately to analyze the visualization of emergent higher level structures, document relatedness at lower level and at the same time show the presence of micro-clusters.
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Singh, P.K., Machavolu, M., Bharti, K., Suda, R. (2012). Analysis of Text Cluster Visualization in Emergent Self Organizing Maps Using Unigrams and Its Variations after Introducing Bigrams. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_89
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DOI: https://doi.org/10.1007/978-81-322-0491-6_89
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