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

By Mehmed Kantardzic(auth.)

Content material:
Chapter 1 Data?Mining strategies (pages 1–25):
Chapter 2 getting ready the knowledge (pages 26–52):
Chapter three facts aid (pages 53–86):
Chapter four studying from info (pages 87–139):
Chapter five Statistical equipment (pages 140–168):
Chapter 6 selection bushes and choice principles (pages 169–198):
Chapter 7 man made Neural Networks (pages 199–234):
Chapter eight Ensemble studying (pages 235–248):
Chapter nine Cluster research (pages 249–279):
Chapter 10 organization principles (pages 280–299):
Chapter eleven internet 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 common sense (pages 414–446):
Chapter 15 Visualization equipment (pages 447–469):

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Data Mining: Concepts, Models, Methods, and Algorithms, Second Edition

Content material: bankruptcy 1 information? Mining techniques (pages 1–25): bankruptcy 2 getting ready the knowledge (pages 26–52): bankruptcy three information aid (pages 53–86): bankruptcy four studying from facts (pages 87–139): bankruptcy five Statistical tools (pages 140–168): bankruptcy 6 selection bushes and determination ideas (pages 169–198): bankruptcy 7 man made Neural Networks (pages 199–234): bankruptcy eight Ensemble studying (pages 235–248): bankruptcy nine Cluster research (pages 249–279): bankruptcy 10 organization principles (pages 280–299): bankruptcy eleven net Mining and textual content Mining (pages 300–327): bankruptcy 12 Advances in facts Mining (pages 328–384): bankruptcy thirteen Genetic Algorithms (pages 385–413): bankruptcy 14 Fuzzy units and Fuzzy common sense (pages 414–446): bankruptcy 15 Visualization equipment (pages 447–469):

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

Example text

Available samples and also future cases may have values missing. Some of the data-mining methods accept missing values and satisfactorily process data to reach a final conclusion. Other methods require that all values be available. An obvious question is whether these missing values can be filled in during data preparation, prior to the application of the data-mining methods. The simplest solution for this problem is the reduction of the data set and the elimination of all samples with missing values.

Opinion, grades < or > Interval Uses units of measurement, but the origin is arbitrary. Celsius or Fahrenheit, calendar dates + or − Ratio Uses units of measurement, and the origin is not arbitrary. 1. Variable types with examples. characteristics as the other numeric data have. Another example of a nominal attribute is the zip code field available in many data sets. In both examples, the numbers used to designate different attribute values have no particular order and no necessary relation to one another.

Important legal restrictions and guidelines, and security and privacy aspects of data mining applications are also introduced in this chapter. Most of the techniques explained in Chapters 13 and 14, about genetic algorithms and fuzzy systems, are not directly applicable in mining large data sets. Recent advances in the field show that these technologies, derived from soft computing, are becoming more important in better representing and computing data as they are combined with other techniques.

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