X-means: Extending K-means with Efficient Estimation of the Number of Clusters
(Citations: 403)
Despite its popularity for general clustering,K-means suffers three major shortcomings;it scales poorly computationally, the numberof clusters K has to be supplied by theuser, and the search is prone to local minima.We propose solutions for the first twoproblems, and a partial remedy for the third.Building on prior work for algorithmic accelerationthat is not based on approximation,we introduce a new algorithm that efficiently,searches the space of cluster locations and...