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Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
 
 
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Swarm Intelligence in Data Mining (Studies in Computational Intelligence) [Hardcover]

Ajith Abraham (Editor), Crina Grosan (Editor), Vitorino Ramos (Editor)

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Book Description

3540349553 978-3540349556 October 5, 2006 1
This volume examines the application of swarm intelligence in data mining, addressing the issues of swarm intelligence and data mining using novel intelligent approaches. The book comprises 11 chapters including an introduction reviewing fundamental definitions and important research challenges. Important features include a detailed overview of swarm intelligence and data mining paradigms, focused coverage of timely, advanced data mining topics, state-of-the-art theoretical research and application developments and contributions by pioneers in the field.

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From the Back Cover

Swarm Intelligence is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Data Mining is an analytic process designed to explore large amounts of data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. This book deals with the application of swarm intelligence in data mining. Addressing the various issues of swarm intelligence and data mining using different intelligent approaches is the novelty of this edited volume. This volume comprises of 11 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. Important features include the detailed overview of the various swarm intelligence and data mining paradigms, excellent coverage of timely, advanced data mining topics, state-of-the-art theoretical research and application developments and chapters authored by pioneers in the field. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

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Inside This Book (learn more)
First Sentence:
Swarm behavior can be seen in bird flocks, fish schools, as well as in insects like mosquitoes and midges. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nearest similar neighbor, flock algorithms, final rulebase, ant colony clustering, clustering validity index, simple propositional rules, fuzzy ants, color image quantization, heuristic desirability, personal best position, dissimilar neighbor, swarm clustering, news foragers, clustering ants, ant colony optimization algorithms, linguistic fuzzy rules, particle swarm optimization, test image set, network connection data, pheromone levels, fuzzy rulebase, partitional clustering algorithms, ant colony algorithm, artificial bees, cluster entropy
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Computational Intelligence, Saturday Morning Problem, International Workshop, New York, San Francisco, Department of Computer Science, Morgan Kaufmann Publishers, Lecture Notes, University of California, Parallel Problem Solving, Service Center, Swarm Clustering Based, Asia-Pacific Conference, Credit History, Lake Tahoe, Multiple Classifier Systems, North American Fuzzy Information Processing Society, Springer Verlag, Checking Account, Future Generation Computer Systems, Hybrid Intelligent Systems, Kluwer Academic Publishers, Neural Computation, San Diego, Simulation of Adaptive Behaviour
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