Data Mining: Concepts, Models, Methods, and Algorithms, by Mehmed Kantardzic(auth.)

By Mehmed Kantardzic(auth.)

Content material:
Chapter 1 Data?Mining options (pages 1–25):
Chapter 2 getting ready the information (pages 26–52):
Chapter three information aid (pages 53–86):
Chapter four studying from information (pages 87–139):
Chapter five Statistical equipment (pages 140–168):
Chapter 6 choice timber and choice principles (pages 169–198):
Chapter 7 synthetic Neural Networks (pages 199–234):
Chapter eight Ensemble studying (pages 235–248):
Chapter nine Cluster research (pages 249–279):
Chapter 10 organization ideas (pages 280–299):
Chapter eleven net Mining and textual content Mining (pages 300–327):
Chapter 12 Advances in info Mining (pages 328–384):
Chapter thirteen Genetic Algorithms (pages 385–413):
Chapter 14 Fuzzy units and Fuzzy good judgment (pages 414–446):
Chapter 15 Visualization equipment (pages 447–469):

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Extra info for Data Mining: Concepts, Models, Methods, and Algorithms, Second Edition

Example text

A larger radius is needed to enclose a fraction of the data points in a highdimensional space. For a given fraction of samples, it is possible to determine the edge length e of the hypercube using the formula e ( p ) = p1/ d where p is the prespecified fraction of samples, and d is the number of dimensions. 3. Regions enclose 10% of the samples for one-, two-, and three-dimensional spaces. 80. 3. This shows that a very large neighborhood is required to capture even a small portion of the data in a high-dimensional space.

It is very important to examine the data thoroughly before undertaking any further steps in formal analysis. Traditionally, data-mining analysts had to familiarize themselves with their data before beginning to model them or use them with some datamining algorithms. However, with the large size of modern data sets, this is less feasible or even entirely impossible in many cases. Here we must rely on computer programs to check the data for us. Distorted data, incorrect choice of steps in methodology, misapplication of datamining tools, too idealized a model, a model that goes beyond the various sources of uncertainty and ambiguity in the data—all these represent possibilities for taking the wrong direction in a data-mining process.

This category includes manipulation of data that are focused on one field at a time, without taking into account their values in related fields. Examples include changing the data type of a field or replacing an encoded field value with a decoded value. 2. Cleansing and Scrubbing. These transformations ensure consistent formatting and usage of a field, or of related groups of fields. This can include a proper formatting of address information, for example. This class of transformations also includes checks for valid values in a particular field, usually checking the range or choosing from an enumerated list.

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