Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-clustering tool, P-CLUSTER, designed to execute on a network of workstations. Experiments involving the unsupervised segmentation of standard texture images were performed. For some data sets, a 96 percent reduction in co...

Inferences acquired by applying clustering analysis of microarrays cannot be reliably assessed before data-originated errors are quantified, an exacting task that is often not performed. Here, we present a novel and fast clustering technique, pair-wise Gaussian merging (PGM), suited for this purpose. Designed for systems with normally distributed error, PGM treats each observation as a Gaussian...

This paper presents the results of a performance study of parallel data clustering on Network of Workstations (NOW) platforms. The clustering program, P-CLUSTER, is based on the mean square-error clustering algorithm and is applied to the problem of image segmentation. The parallel implementation uses a client-server model, in which the clustering task is divided among a set of clients that rep...

In this paper I consider the problem of clustering the cepstrum coefficients of an acoustic vector into a number of disjoint sets (subvectors) using the mutual information as the clustering criterion. I then quantize each one of the subvectors independently using different quantization step. I compare the performance of the clustering scheme with a heuristic one where neighboring coefficients a...

It is shown that a particular case of the Bayesian Ying–Yang learning system and theory reduces to the maximum likelihood learning of a finite mixture, from which we have obtained not only the EM algorithm for its parameter estimation Ž and its various approximate but fast algorithms for clustering in general cases including Mahalanobis distance clustering or . elliptic clustering , but also cr...

This paper describes the new features available in the SimPoint 3.0 release. The release provides two techniques for drastically reducing the run-time of SimPoint: faster searching to find the best clustering, and efficiently clustering large numbers of intervals. SimPoint 3.0 also provides an option to output only the simulation points that represent the majority of execution, which can reduce...

The clustering algorithm employing “stochastic association”, which we have already proposed, offers a simple and efficient soft-max adaptation rule. The adaptation process is the same as the on-line K-means clustering method except for adding random fluctuation in the distortion error evaluation process. This paper describes VLSI implementation of this new clustering algorithm based on a pulse ...

This paper presents a decision tree pruning method for the model clustering of HMM-based parametric speech synthesis by cross-validation (CV) under the minimum generation error (MGE) criterion. Decision-tree-based model clustering is an important component in the training process of an HMM based speech synthesis system. Conventionally, the maximum likelihood (ML) criterion is employed to choose...

This paper describes the new features available in the SimPoint 3.0 release. The release provides two techniques for drastically reducing the run-time of SimPoint: faster searching to find the best clustering, and efficiently clustering large numbers of intervals. SimPoint 3.0 also provides an option to output only the simulation points that represent the majority of execution, which can reduce...

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sa...