Self-regulated incremental clustering with focused preferences
Due to their online learning nature, incremental clustering techniques can handle a continuous stream of data. In particular, various incremental clustering techniques based on Adaptive Resonance Theory (ART) have been shown to have low computational complexity in adaptive learning and are less sens...
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Main Authors: | WANG, Di, TAN, Ah-hwee |
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Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2016
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Online Access: | https://ink.library.smu.edu.sg/sis_research/5478 https://ink.library.smu.edu.sg/context/sis_research/article/6481/viewcontent/Self_Regulated_Incremental_Clustering_with_Focused_Preferences_accepted.pdf |
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Institution: | Singapore Management University |
Language: | English |
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