Abstract
Self-Organizing Map (SOM) networks have been successfully applied as a clustering method to numeric datasets. However, it is not feasible to directly apply SOM for clustering transactional data. This paper proposes the Transactions Clustering using SOM (TCSOM) algorithm for clustering binary transactional data. In the TCSOM algorithm, a normalized Dot Product norm based dissimilarity measure is utilized for measuring the distance between input vector and output neuron. And a modified weight adaptation function is employed for adjusting weights of the winner and its neighbors. More importantly, TCSOM is a one-pass algorithm, which is extremely suitable for data mining applications. Experimental results on real datasets show that TCSOM algorithm is superior to those state-of-the-art transactional data clustering algorithms with respect to clustering accuracy.
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He, Z., Xu, X. & Deng, S. TCSOM: Clustering Transactions Using Self-Organizing Map. Neural Process Lett 22, 249–262 (2005). https://doi.org/10.1007/s11063-005-8016-3
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DOI: https://doi.org/10.1007/s11063-005-8016-3