Hypergraph Clustering based on Game Theory
نویسندگان
چکیده
Data clustering considers the problem of grouping data into clusters based on its similarity measure. It is one of the central problems for data analysis, with a wide applications to a variety of areas such as marketing research, data mining and social behavior analysis.Real world objects can be modeled as points in a high dimensional metric. So clustering these real world data is corresponded to assign each point to a cluster label. A classic approach to clustering is called K-means algorithm which randomly selects k initial cluster centers and iteratively assigns each data point and updates the cluster centers. A lot of approaches like K-means algorithm considers the pairwise distance as a similarity measurement. In that case, the clustering result is produced by optimizing an objective function which maximizes the distances between pairs of points from different clusters and simultaneously minimizes the distances between pairs of points from the same clusters. Fig.1 shows an example of clustering points into 3 clusters based on their pairwise similarity measure.
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تاریخ انتشار 2014