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In statistics and data mining, affinity propagation (AP) is a clustering algorithm based on the concept of "message passing" between data points. Unlike clustering algorithms such as or , AP does not require the number of clusters to be determined or estimated before running the algorithm. Like -medoids, AP finds "exemplars", members of the input set that are representative of clusters.〔 ==Algorithm== Let through be a set of data points, with no assumptions made about their internal structure, and let be a function that quantifies the similarity between any two points, such that iff is more similar to than to . The algorithm proceeds by alternating two message passing steps, to update two matrices:〔 * The "responsibility" matrix has values that quantify how well-suited is to serve as the exemplar for , relative to other candidate exemplars for . * The "availability" matrix contains values represents how "appropriate" it would be for to pick as its exemplar, taking into account other points' preference for as an exemplar. Both matrices are initialized to all zeroes, and can be viewed as log-probability tables. The algorithm then performs the following updates iteratively: * First, responsibility updates are sent around: * Then, availability is updated per ::. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Affinity propagation」の詳細全文を読む スポンサード リンク
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