Clustering domain is vital part of data mining domain and widely used in different applications. In this project we are focusing on affinity propagation (AP) clustering which is presented recently to overcome many clustering problems in different clustering applications. Many clustering applications are based on static data. AP clustering approach is supporting only static data applications, he...

This paper presents a new algorithm for linear text segmentation. It is an adaptation of Affinity Propagation, a state-of-the-art clustering algorithm in the framework of factor graphs. Affinity Propagation for Segmentation, or APS, receives a set of pairwise similarities between data points and produces segment boundaries and segment centres – data points which best describe all other data poi...

It is challenging to find a small set of data points, so-called exemplars or landmarks, that are nicely representative of itself and other data points. Affinity propagation (AP) is an effective algorithm that identifies exemplars among data points by recursively sending realvalued messages between pairs of data points. AP calculates the message using the similarity among data points. Hence, the...

A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predicta...

Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these matrices to choose cluster centers (or exemplars) of respective clusters. H...