The current data tends to be more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a smaller data in volume and has the same analytical results as the original representation. A clustering process needs data reduction to obtain an efficient processing time while clustering and mitigate curse of dimensionality. This pap...

We present the IBM systems for the Rich Transcription 2007 (RT07) speaker diarization evaluation task on lecture meeting data. We first overview our baseline system that was developed last year, as part of our speech-to-text system for the RT06s evaluation. We then present a number of simple schemes considered this year in our effort to improve speaker diarization performance, namely: (i) A bet...

We describe a generalization of the cluster-state model of quantum computation to continuous-variable systems, along with a proposal for an optical implementation using squeezed-light sources, linear optics, and homodyne detection. For universal quantum computation, a nonlinear element is required. This can be satisfied by adding to the toolbox any single-mode non-Gaussian measurement, while th...

This article introduces clusteff, a new Stata command for checking the severity of cluster heterogeneity in cluster robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2015) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion...

A closed-form expression for the effect of cluster scavenging on the rate of homogeneous nucleation of a vapor in the presence of continuum regime particles is obtained by solving the kinetic equation of nucleation by the method of singular perturbation. The reduction in nucleation rate of a condensing species at a given supersaturation is shown to be dependent largely on the number concentrati...

We present a model that explains how a cluster moves through a life cycle and why this movement differs from the industry life cycle. The model is based on three key processes: the changing heterogeneity in the cluster describes the movement of the cluster through the life cycle; the geographical absorptive capacity enables clustered companies to take advantage of a larger diversity of knowledg...

In this paper we present cluster canonical correlation analysis (cluster-CCA) for joint dimensionality reduction of two sets of data points. Unlike the standard pairwise correspondence between the data points, in our problem each set is partitioned into multiple clusters or classes, where the class labels define correspondences between the sets. Cluster-CCA is able to learn discriminant low dim...

In this paper, we propose a cluster-based cumulative representation for cluster ensembles. Cluster labels are mapped to incrementally accumulated clusters, and a matching criterion based on maximum similarity is used. The ensemble method is investigated with bootstrap re-sampling, where the k-means algorithm is used to generate high granularity clusterings. For combining, group average hierarch...

In this paper, we consider the problem of synthesizing custom Networks-on-Chip (NoC) architectures that are optimized for a given application. Both unicast and multicast traffic flows are considered in the input specification. We formulate the joint multicast routing and network design problem using a rip-up and reroute procedure, where each multicast routing step is formulated as a minimum dir...

A combination of gene ranking, dimensional reduction, and recursive feature elimination (RFE) using a BP-MLP artificial neural network (ANN) was used to select genes for DNA microarray classification. Use of k-means cluster analysis for dimensional reduction and maximum sensitivity for RFE resulted in 64-gene models with fewer invariant and correlated features when compared with PCA and mimimum...