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.

Reports recent research results on Swarm Intelligence in Data Mining Includes supplementary material: sn.pub/extras

Autorentext
Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).

Inhalt
Swarm Intelligence in Data Mining.- Ants Constructing Rule-Based Classifiers.- Performing Feature Selection with ACO.- Simultaneous Ant Colony Optimization Algorithms for Learning Linguistic Fuzzy Rules.- Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection.- Particle Swarm Optimization for Pattern Recognition and Image Processing.- Data and Text Mining with Hierarchical Clustering Ants.- Swarm Clustering Based on Flowers Pollination by Artificial Bees.- Computer study of the evolution of 'news foragers' on the Internet.- Data Swarm Clustering.- Clustering Ensemble Using ANT and ART.