This paper presents an algorithm based on the Growing Self Organizing Map (GSOM) called the High Dimensional Growing Self Organizing Map with Randomness (HDGSOMr) that can cluster massive high dimensional data efficiently. The original GSOM algorithm is altered to accommodate for the issues related to massive high dimensional data. These modifications are presented in detail with experimental results of a massive real-world dataset.
Book title: Neural information processing
Field of Research
080499 Data Format not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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