The determination of the number of groups in a dataset, their composition and the most relevant measurements to be considered in clustering the data, is a high-demanding task, especially when the a priori information on the dataset is limited. Some different genetic approaches are proposed as tools for automatic data clustering and features selection. They differ in the adopted codification of ...

We propose a new technique called Di erential 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 Di erential Power Analysis. It captures more general leakage features and can be applied to algorithmic collisions as well as implementation speci c collisions...

Cluster analysis is a way of “slicing and dicing” data to allow the grouping together of similar entities and the separation of dissimilar ones. Issues arise due to the existence of a diverse number of clustering algorithms, each with different techniques and inputs, and with no universally optimal methodology. Thus, a framework for cluster analysis and validation methods are needed. Our approa...

clustering technique is one of the most important techniques of data mining and is the branch of multivariate statistical analysis and a method for grouping similar data in to same clusters. with the databases getting bigger, the researchers try to find efficient and effective clustering methods so that they can make fast and real decisions. thus, in this paper, we proposed an improved ant syst...

background: this cross sectional comparative study was performed to compare the mean fluorescence intensity of cluster of differentiation 45 between healthy individuals and patients with acute lymphoblastic leukemia. patients and methods: thirty three healthy individuals (mean age 11 years) and 41 patients with b cell acute lymphoblastic leukemia (mean age 7 years) were enrolled in the study in...

We present a systematic comparison of some usual estimators of the 2–point correlation function, some of them currently used in Cosmology, others extensively employed in the field of the statistical analysis of point processes. At small scales, it is known that the correlation function follows reasonably well a power–law expression ξ(r) ∝ r−γ. The accurate determination of the exponent γ (the o...

Mixtures of factor analyzers enable model-based density estimation and clustering to be undertaken for high-dimensional data, where the number of observations n is very large relative to their dimension p. In practice, there is often the need to reduce further the number of parameters in the specification of the componentcovariance matrices. To this end, we propose the use of common component-f...

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...