The paper derives (with a flawed Lebesgue-integral argument) that entropy minimization performs two-level set-cover clustering and introduces URECA, a union-find clustering loss that improves few-shot code-search adaptation.
An Overview on Clustering Methods
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abstract
Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.
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URECA: The Chain of Two Minimum Set Cover Problems exists behind Adaptation to Shifts in Semantic Code Search
The paper derives (with a flawed Lebesgue-integral argument) that entropy minimization performs two-level set-cover clustering and introduces URECA, a union-find clustering loss that improves few-shot code-search adaptation.