We study a simple abstract problem motivated by a variety of applications in protein sequence analysis. Consider a string of 0s and 1s of length L, and containing D 1s. If we believe that some or all of the 1s may be clustered near the start of the sequence, which subset is the most significantly so clustered, and how significant is this clustering? We approach this question using the minimum d...

This section provides an overview of the San Diego Association of Government's methodology for defining and analyzing industrial clusters.

Spectral clustering is a family of methods to find K clusters using the eigenvectors of a matrix. Typically, this matrix is derived from a set of pairwise similarities Sij between the points to be clustered. This task is called similarity based clustering, graph clustering, or clustering of diadic data. One remarkable advantage of spectral clustering is its ability to cluster “points” which are...

In this paper, we show that the popular K-means clustering problem can equivalently be reformulated as a conic program of polynomial size. The arising convex optimization problem is NP-hard, but amenable to a tractable semidefinite programming (SDP) relaxation that is tighter than the current SDP relaxation schemes in the literature. In contrast to the existing schemes, our proposed SDP formula...

What is Clustering? Clustering is the process of making group of abstract objects into classes of similar objects. Points to Remember  A cluster of data objects can be treated as a one group.  While doing the cluster analysis, we first partition the set of data into groups based on data similarity and then assign the label to the groups.  The main advantage of Clustering over classification ...

We propose a new technique called Differential Cluster Analysis for side-channel key recovery attacks. This technique uses cluster analysis to detect internal collisions and it combines features from previously known collision attacks and Differential Power Analysis. It captures more general leakage features and can be applied to algorithmic collisions as well as implementation specific collisi...

In Cluster analysis, we seek to identify the “natural” structure of groups based on a multivariate profile, if it exists, which both minimises the within-group variation and maximises the between-group variation. The objective is to perform data reduction into manageable bite-sizes which could be used in further analysis or developing hypothesis concerning the nature of the data. It is explorat...

A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved. In a unified framework, a brief review of alternative methods is provided and we show that the proposed method is equivalent t...

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...