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

By Mehmed Kantardzic

This e-book stories state of the art methodologies and methods for interpreting thousands of uncooked info in high-dimensional information areas, to extract new details for choice making. The goal of this booklet is to provide a unmarried introductory resource, prepared in a scientific manner, within which lets direct the readers in research of enormous info units, throughout the rationalization of simple innovations, versions and methodologies built in contemporary a long time.

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**Extra resources for Data Mining. Concepts, Models, Methods, and Algorithms**

**Example text**

A11 represents the number of samples in the first interval belonging to the first class, A12 is the number of samples in the first interval belonging to the second class, A21 is the number of samples in the second interval belonging to the first class, and finally A22 is the number of samples in the second interval belonging to the second class. We will analyze the ChiMerge algorithm using one relatively simple example, where the database consists of 12 two-dimensional samples with only one continuous feature (F) and an output classification feature (K).

The distance measure D is small for close samples (close to zero) and large for distinct pairs (close to one). When the features are numeric, the similarity measure S of two samples can be defined as where Dij is the distance between samples xi and xj and α is a parameter mathematically expressed as D is the average distance among samples in the data set. Hence, α is determined by the data. 5. Normalized Euclidean distance measure is used to calculate the distance Dij between two samples xi and xj: where n is the number of dimensions and maxk and mink are maximum and minimum values used for normalization of the k-th dimension.

Standard deviation normalization. e. Compare the results of previous normalizations and discuss the advantages and disadvantages of different techniques. 7. Perform data smoothing using a simple rounding technique for a data set: and present the new data set when the rounding is performed to the precision of: a. 1 b. 1. 8. Given a set of four-dimensional samples with missing values: ♦ X1 = {0, 1, 1, 2} ♦ X2 = {2, 1, −, 1} ♦ X3 = {1, −, −, 0} ♦ X4 = {−, 2, 1, −} 32 Chapter 2: Preparing the Data Chapter 2: Preparing the Data 33 if the domains for all attributes are [0, 1, 2], what will be the number of "artificial" samples if missing values are interpreted as "don't care values" and they are replaced with all possible values for a given domain.