Principles of Data Mining

The growing interest in data mining is motivated by a common problem
across disciplines: how does one store, access, model, and ultimately
describe and understand very large data sets? Historically, different
aspects of data mining have been addressed independently by different
disciplines. This is the first truly interdisciplinary text on data
mining, blending the contributions of information science, computer
science, and statistics.


The book consists of three sections. The first, foundations, provides a
tutorial overview of the principles underlying data mining algorithms
and their application. The presentation emphasizes intuition rather
than rigor. The second section, data mining algorithms, shows how
algorithms are constructed to solve specific problems in a principled
manner. 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” models. The third section shows how all of the
preceding analysis fits together when applied to real-world data mining
problems. Topics include the role of metadata, how to handle missing
data, and data preprocessing.



Download Here
Password: www.ebooksportal.org