A Maximum Variance Cluster Algorithm

Incrementally Assessing Cluster Tendencies with a Maximum Variance Cluster Algorithm

A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the benefits of the plain clustering algorithm with regard to other approaches for clustering. Experiments using both synthetic and real data have been performed in order to evaluate the differences between the proposed me...

A Maximum Variance Cluster Algorithm

A New Algorithm for Cluster Initialization

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximu...

Origins and extensions of the k-means algorithm in cluster analysis

This paper presents a historical view of the well-known k-means algorithm that aims at minimizing (approximately) the classical SSQ or variance criterion in cluster analysis . We show to which authors the different (discrete and continuous) versions of this algorithm can be traced back, and which were the underlying applications. Moreover, the paper describes a series of extensions and generali...

Spectral Clustering by Recursive

In this paper, we analyze the second eigenvector technique of spectral partitioning on the planted partition random graph model, by constructing a recursive algorithm using the second eigenvectors in order to learn the planted partitions. The correctness of our algorithm is not based on the ratio-cut interpretation of the second eigenvector, but exploits instead the stability of the eigenvector...