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

By Mehmed Kantardzic

This e-book reports state of the art methodologies and methods for examining hundreds and hundreds of uncooked facts in high-dimensional information areas, to extract new details for choice making. The goal of this publication is to provide a unmarried introductory resource, prepared in a scientific method, within which shall we direct the readers in research of huge information units, throughout the clarification of uncomplicated suggestions, types and methodologies constructed in fresh many years.


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Example text

Formally, these steps are described with the following computations applied to the feature value X: 1. Integer divising Y = int (X/10k) 2. Rounding If(mod (X, 10k) ≥ (10k/2)) then Y = Y + 1 3. Integer multiplication X = Y*10k where k is the number of rightmost decimal places to round. For example, the number 1453 is rounded to 1450 if k = 1, rounded to 1500 if k = 2, and rounded to 1000 if k = 3. Given a number of values for a feature as an input parameter of the procedure, this simple rounding algorithm can be applied in iterations to reduce these values in large data sets.

6. 11}, normalize the data set using a. Decimal scaling on interval [−1, 1]. b. Min-max normalization on interval [0, 1]. c. Min-max normalization on interval [−1, 1]. d. 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.

If we settle for a less-than optimal answer, the algorithm's complexity can be reduced to the linear level, using a sequential approach. Using the greedy method, the algorithm reduces the size sequentially, sample by sample (or subset by subset), by selecting at each step the one that causes the greatest decrease in the total variance. 7 REVIEW QUESTIONS AND PROBLEMS 1. 1 2. If one attribute in the data set is student-grade with values A, B, C, D, and F, what type is these attribute values? Give a recommendation for preprocessing of the given attribute.

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