Salience-aware adaptive resonance theory for large-scale sparse data clustering

• Main Authors: MENG, Lei, TAN, Ah-hwee, MIAO, Chunyan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Adaptive resonance theory; Clustering; Sparse data; Subspace learning; Feature weighting; Parameter adaptation; Databases and Information Systems; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5240; https://ink.library.smu.edu.sg/context/sis_research/article/6243/viewcontent/1_s2.0_S0893608019302758_main.pdf
• Institution: Singapore Management University

Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature...