Clustering algorithms have emerged as an alternative powerful meta-learning tool to
accurately analyze the massive volume of data generated by modern applications. In …

In this article, we evaluate the performance of three clustering algorithms, hard K-Means,
single linkage, and a simulated annealing (SA) based technique, in conjunction with four …

In this paper, we aim to compare empirically four initialization methods for the K-Means
algorithm: random, Forgy, MacQueen and Kaufman. Although this algorithm is known for its …

This volume encompasses many new types of constraints and clustering methods as well as
delivers thorough coverage of the capabilities and limitations of constrained clustering. With …

We investigate variants of Lloyd's heuristic for clustering high-dimensional data in an attempt
to explain its popularity (a half century after its introduction) among practitioners, and in …

We present a prototype system, code-named Pulse, for mining topics and sentiment
orientation jointly from free text customer feedback. We describe the application of the …

We consider the following general correlation-clustering problem [N. Bansal, A. Blum, S.
Chawla, Correlation clustering, in: Proc. 43rd Annu. IEEE Symp. on Foundations of …

Abstract Model-based clustering techniques have been widely used and have shown
promising results in many applications involving complex data. This paper presents a unified …

We compare the three basic algorithms for model-based clustering on high-dimensional
discrete-variable datasets. All three algorithms use the same underlying model: a naive …

It is well-known that for high dimensional data clustering, standard algorithms such as EM
and K-means are often trapped in a local minimum. Many initialization methods have been …