As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.

Data Mining Techniques addresses all the major and latest techniques of data mining and data warehousing. It deals in detail with the latest algorithms for discovering association rules, decision trees, clustering, neural networks and genetic algorithms.

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Machine Learning and Security. At Kobo, we try to ensure that published reviews do not contain rude or profane language, spoilers, or any of our reviewer’s personal information. Applied Cryptography and Network Security. This book can serve as a textbook for students of computer science, mathematical science and management science. Advances in Databases and Information Systems. Apache Spark Machine Learning Blueprints. Database and Expert Systems Applications.

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Pujari – Google Books Machine Learning for Developers. Mastering Text Mining with R. Python Machine Learning By Example. Readings in Artificial Intelligence and Software Engineering. Data Mining Techniques – Arun K. Pujari The discussion on association rule mining has been extended to include rapid association rule mining RARMFP-Tree Growth Algorithm for discovering association rule and the Eclat and dEclat algorithms.

The Theory of Info-Dynamics: Computational Intelligence in Data Mining. Machine Learning for Evolution Strategies.

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The book also discusses the mining of web data, spatial data, temporal data and text data. Machine Learning in Python.