A Novel Self-Adaptive Clustering Algorithm for Dynamic Data
Along with the fast advance of internet technique, internet users have to deal with novel data every day. For most of them, one of the most useful knowledge exploited from web is about the transfer of the information expressed by dynamically updated data. Unfortunately, traditional algorithms often cluster novel data without considering the existent clustering model. They have to cluster input data over again, once input data are updated. Hence, they are time-consuming and inefficient. For efficiently clustering dynamic data, a novel Self-Adaptive Clustering algorithm (abbreviated as SAC) is proposed in this paper. SAC comes from Self Organizing Mapping algorithm (abbreviated as SOM), whereas, it doesn’t need to make any assumption about neuron topology beforehand. Besides, when input data are updated, its topology remodels meanwhile. Experiment results demonstrate that SAC can automatically tune its topology along with the update of input data.
KeywordsSelf-adaptive algorithm Competitive learning Minimum spanning tree Self-organizing-mapping
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