By Huntington E. V.

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Temporal Data Mining. : Multidimensional Clustering Algorithms. : Introduction to Optimization. : Learning with Kernels. : Information Visualization. : Exploratory Data Analysis. : Estimation of Dependences Based on Empirical Data, 2d edn. : Statistical Pattern Recognition. : Text Mining: Predictive Methods for Analyzing Unstructured Information. : Multimedia Data Mining. 1 Quantitative Feature: Distribution and Histogram 1D data is a set of entities represented by one feature, categorical or quantitative.

This involves an assumption that each observation xi is modeled by the distribution f(xi ) so that the mean’s model is the average of distributions f(xi ). The population analogues to the mean and variance are defined over function f(x) so that the mean, median and the midrange are unbiased estimates of the population mean. Moreover, the variance of the mean is N times less than the population variance, so that the standard deviation tends to decrease by N when N grows. 6) where C stands for a constant term equal to C = (2π σ 2 )−1/2 .

The divider between the latter groups is taken between Tavistock (10,222) and Bodmin (12,553). In this way, we get three or four groups of towns for the purposes of social monitoring. Is this enough, regarding the other features available? Are the groups, defined in terms of population size only, homogeneous enough for the purposes of monitoring? As further computations will show, the numbers of services on average do follow the town sizes, but this set (as well as the complete set of about thirteen hundred England Market towns) is much better represented with seven somewhat different clusters: large towns of about 17–20,000 inhabitants, two clusters of medium sized towns (8–10,000 inhabitants), three clusters of small towns (about 5,000 inhabitants), and a cluster of very small settlements with about 2,500 inhabitants.