Data Mining. Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic

By Mehmed Kantardzic

This e-book experiences cutting-edge methodologies and methods for reading hundreds and hundreds of uncooked facts in high-dimensional information areas, to extract new details for selection making. The goal of this e-book is to provide a unmarried introductory resource, prepared in a scientific approach, during which shall we direct the readers in research of huge information units, in the course of the clarification of easy ideas, types and methodologies built in fresh a long time.

 

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Topics
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Control
Artificial Intelligence (incl. Robotics)

Extra info for Data Mining. Concepts, Models, Methods, and Algorithms

Example text

Assuming normal distributions of values, it is possible to describe an efficient technique for selecting subsets of features. Two descriptors characterize a multivariate normal distribution: 38 Chapter 3: Data Reduction Chapter 3: Data Reduction 39 1. M - a vector of the m feature means, and 2. C - an m × m covariance matrix of the means, where Ci, i are simply the variance of feature i, and Ci,j terms are correlations between each pair of features where v(k, i) and v(k, j) are the values of features indexed with i and j, m(i) and m(j) are feature means, and n is the number of dimensions.

Assign approximately equal numbers of sorted adjacent values (vi) to each bin, where the number of bins is given in advance. 3. Move a border element vi from one bin to the next (or previous) when that reduces the global distance error (ER) (the sum of all distances from each vi to the mean or mode of its assigned bin). A simple example of the dynamic bin procedure for feature discretization is given next. The set of values for a feature f is {5, 1, 8, 2, 2, 9, 2, 1, 8, 6}. Split them into three bins (k = 3), where the bins will be represented by their modes.

2. For each feature f ∈ F, remove one feature f from F and obtain a subset Ff. Find the difference between entropy for F and entropy for all Ff. 2, we have to compare the differences (EF−EF−F1), (EF − EF−F2), and (EF − EF−F3). 2: The first principal component is an axis in the direction of maximum variance 3. Let fk be a feature such that the difference between entropy for F and entropy for Ffk is minimum. 4. Update the set of features F = F − [Fk}, where − is a difference operation on sets. In our example, if the difference (EF − EF−F1) is minimum, then the reduced set of features is {F2, F3}.

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