This book offers a thorough grounding in machine learning concepts combined with practical advice on applying machine learning tools and techniques in real-world data mining situations. Clearly written and effectively illustrated, this book is ideal for anyone involved at any level in the work of extracting usable knowledge from large collections of data. Complementing the book's instruction is fully functional machine learning software.
P. S. Yu, J. Han, and C. Faloutsos. Link Mining: Models, Algorithms and Applications. New York: Springer, 2010. X. Yin, J. Han, and P. S. Yu. Cross-relational clustering with user's guidance. In Proc. 2005 ACM SIGKDD Int. Conf.
1.4 DATA: PROBABILISTIC VIEW The probabilistic view of the data assumes that each numeric attribute X is a random variable, defined as a function that assigns a real number to each outcome of an experiment (i.e., some process of ...
If the outcomes are numeric, and represent the observed values of the random variable, then X: O → O is simply the identity function: X(v) = v for all v ∈ O. The distinction between the outcomes and the value of the random variable is ...
The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, ...
This book focuses on the importance of clean, well-structured data as the first step to successful data mining.
Temporal data mining deals with the harvesting of useful information from temporal data.
[BERN87] Bernstein, P. et al., “Concurrency Control and Recovery in Database Systems,” Addison Wesley, MA, 1987. ... [BROD88] Brodie, M. et al., “Readings in Artificial Intelligence and Databases,” Morgan Kaufmann, CA, 1988.
The references are divided into seven sub-topics: books based on specific data mining software applications; statistical books; books that include case studies; web mining; ... Data Mining: Know It All. Morgan Kaufmann, Burlington, MA.
This book is the first technical guide to provide a complete, generalized road map for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.
Introduction to data mining -- Association rules -- Classification learning -- Statistics for data mining -- Rough sets and bayes theories -- Neural networks -- Clustering -- Fuzzy information retrieval.