Semi-supervised clustering with logic programming
Read OriginalThis technical article argues that logic programming, specifically Prolog, is well-suited for general clustering tasks, especially when domain knowledge is expressed as rules rather than numerical vectors. It critiques traditional algorithms like k-means for their limitations with non-commensurate data and proposes artisanal Prolog clustering algorithms as a more intuitive, rule-based alternative for scenarios like customer segmentation.
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