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Principles of Data Mining


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Flowers that grow in data mining is motivated by a common problem across disciplines: how does one store, access, model, and finally describe and understand very large data sets? The second section, data mining algorithms, shows how algorithms developed to solve specific problems with a principled way. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory based" model. Topics include the role of metadata, how to handle missing data, and data preprocessing.

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  • ISBN-10: 026208290X
  • ISBN-13: 9780262082907
  • Publisher: The MIT Press
  • Pages: 578
  • Date: August 2001

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algorithm computation data mining learning machine learning mathematics mining neural network neural networks statistics text mining science computational neuroscience applied mathematics formal sciences linear regression

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