By Mehmed Kantardzic(auth.)
Chapter 1 Data?Mining thoughts (pages 1–25):
Chapter 2 getting ready the information (pages 26–52):
Chapter three facts relief (pages 53–86):
Chapter four studying from facts (pages 87–139):
Chapter five Statistical tools (pages 140–168):
Chapter 6 selection bushes and determination ideas (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 principles (pages 280–299):
Chapter eleven net Mining and textual content Mining (pages 300–327):
Chapter 12 Advances in facts 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 tools (pages 447–469):
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Additional resources for Data Mining: Concepts, Models, Methods, and Algorithms, Second Edition
To increase our understanding of data-mining techniques and their limitations, it is crucial to analyze not only successful but also unsuccessful applications. Failures or dead ends also provide valuable input for data-mining research and applications. We have to underscore the intensive conflicts that have arisen between practitioners of “digital discovery” and classical, experience-driven human analysts objecting to these intrusions into their hallowed turf. S. economist Orley Ashenfelter, who used data-mining techniques to analyze the quality of French Bordeaux wines.
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.
Therefore, the normalization parameters must be saved along with a solution. 2 Data Smoothing A numeric feature, y, may range over many distinct values, sometimes as many as the number of training cases. For many data-mining techniques, minor differences among these values are not significant and may degrade the performance of the method and the final results. They may be considered as random variations of the same underlying value. Hence, it can be advantageous sometimes to smooth the values of the variable.