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Methods for cluster generation are as diverse as the structures which they are applied to [1], may they be e.g. similarity- or substructure-based.
The methods for cluster analysis present in literature can be roughly classified into two main families: probability-based methods (see e.g. [1]), which are based on the assumption that clusters come from a mixture of distributions, from a given family.
Several methods for cluster analysis are available and results from questionnaires are suitable for dividing participants into subsets.
The Kulldorff's scan statistic is one of the most interesting and used methods for cluster analysis [ 16, 1, 17].
19, 20 Scan statistics are one of the most widely used statistical methods for cluster detection in epidemiology.
Using methods for cluster randomised trials and assuming a coefficient of variation between clusters of 0.2, 80 adults and 114 children per community were required to detect the specified reductions with 80% power at the 5% significance level.
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Evolutionary Monte Carlo Methods for Clustering.
This paper deals with methods for clustering of continuous signals such as time series data sets.
Nevertheless, existing methods for clustering XML documents are designed to work in a centralized way.
While there are many methods for clustering, the single-link hierarchical clustering is one of the most popular techniques.
We also examine the utility of nine electrostatic similarity methods for clustering of barnase alanine-scan mutants.
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