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وصف كتب مصر
The continual explosion of information technology and the need for better data
collection and management methods has made data mining an even more relevant
topic of study. Books on data mining tend to be either broad and introductory
or focus on some very specific technical aspect of the field. This book is a
series of seventeen edited "student-authored lectures" which explore in depth
the core of data mining (classification, clustering and association rules) by
offering overviews that include both analysis and insight. The initial chapters
lay a framework of data mining techniques by explaining some of the basics such
as applications of Bayes Theorem, similarity measures, and decision trees.
Before focusing on the pillars of classification, clustering, and association
rules, this book also considers alternative candidates such as point estimation
and genetic algorithms. The book's discussion of classification includes an
introduction to decision tree algorithms, rule-based algorithms (a popular
alternative to decision trees) and distance-based algorithms. Five of the
lecture-chapters are devoted to the concept of clustering or unsupervised
classification. The functionality of hierarchical and partitional clustering
algorithms is also covered as well as the efficient and scalable clustering
algorithms used in large databases. The concept of association rules in terms
of basic algorithms, parallel and distributive algorithms and advanced measures
that help determine the value of association rules are discussed. The final
chapter discusses algorithms for spatial data mining.