The affinity propagation algorithm is applied to a problem of breast cancer subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. A well know breast cancer case series was used to compare the results of the affinity

by a prototype or model, so affinity-based clustering – and its benefits – cannot be directly realized. We describe a technique called “affinity propagation”, which combines the advantages of both approaches. The method learns a mixture model of the data by recursively propagating affinity messages. We

Abstract. Affinity propagation constitutes an exemplar based clustering technique which reliably optimizes the quantization error given a matrix of pairwise data dissimilarities by means of the max-sum algorithm for factor graphs. Albeit very efficient for sparse matrices, it displays squared

. Motivated by the idea that community detection is in fact a clustering problem on graphs, we propose several similarity metrics of vertex to transform a community detection problem into a clustering problem, and further adopt a recently-proposed clustering method, namely ‘Affinity Propagation’, to extract

Recently, there has been a great deal of interest in analyz-ing inherent structures in posts on microblogs such as Twit-ter. While many works utilize a well-known topic modeling technique, we instead propose to apply Affinity Propaga-tion [4] (AP) to analyze such a corpus, and we hypothesize

Abstract. This paper proposed StrAP (Streaming AP), extending Affinity Propagation (AP) to data steaming. AP, a new clustering algorithm, extracts the data items, or exemplars, that best represent the dataset using a message passing method. Several steps are made to build StrAP. The first one

Affinity Propagation (AP) [1] is a recently introduced algorithm for exemplar-based clustering. The goal of the algorithm is to find good partitions of data and associate each partition with its most prototypical data point (‘exemplar’) such that the similarity between points to their exemplar

Affinity propagation (AP) is a clustering method that can find data centers or clusters by sending messages between pairs of data points. Seed Affinity Propagation is a novel semisupervised text clustering algorithm which is based on AP. AP algorithm couldn’t cope up with part known data direct

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Affinity propagation (AP) is a clustering algorithm which has much better performance than traditional clustering approach such as k-means algorithm. In this paper, we present an algorithm called voting partition affinity propagation (voting-PAP) which is a method for clustering using evidence