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Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics (Advanced Information and Knowledge Processing)

Dan A. Simovici, Chabane Djeraba




[PDF.ns35] Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics (Advanced Information and Knowledge Processing)

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 | #9147623 in Books |  2008-08-15 | Original language:English | PDF # 1 |  9.21 x1.38 x6.14l,2.32 | File type: PDF | 616 pages

||1 of 1 people found the following review helpful.| Vast, dense, and concise|By Joseph A. Dudar|It's correct that this book has typesetting errors in the symbolic math. After sampling and exploring various regions of the book I only found one section with an error: the set theoretical foundational material. I have may books that start this way, and it's useful to see the notation and concepts the author will use later in the boo|From the Back Cover||The maturing of the field of data mining has brought about an increased level of mathematical sophistication. Such disciplines like topology, combinatorics, partially ordered sets and their associated algebraic structures (lattices and Boole

This book integrates the mathematics of data mining with its applications, offering the reader a reference to the mathematical tools required for data mining.

Dedicated to the study of set-theoretical foundations of data mining, this book is focused on set theory and several closely related areas: partially ordered sets and lattice theory, metric spaces and combinatorics. The book is structured into 4 parts and presents a comprehensive discussion of the subject...

You easily download any file type for your device.Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics (Advanced Information and Knowledge Processing)   |  Dan A. Simovici, Chabane Djeraba. A good, fresh read, highly recommended.

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