Quality Measures in Data Mining (Studies in Computational Intelligence (43))

Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade. This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee. The original Japanese edition of this book, published by Saiensu-sha, Japan, in March 2005, has fortunately acquired a favorable reputation. I am grateful to the readers for their kind feedbacks, many of which are included in this edition. I hope this English publication attracts readers in wider areas, and evokes valuable feedbacks furthermore. In the months after the Japanese publication, researches on the compl- valued neural networks have kept evolution in respective directions. There are some plans of special sessions in international conferences and special issues in journals. The bibliography has been slightly modi?ed to include the special sessions and latest journal publications. On the other hand, references written in Japanese on domestic circumstances have been omitted. Besides, Fig.1.1 has been added, and Fig.2.1 has been modi?ed, which are related to theSpecial Issue on Complex-Valued Neural Networks, The Journal of the IEICE, 87 (6), June 2004 (in Japanese), so that even readers not having glancedtheissuecanobtainclearconcepts.Withthesemodi?cations,Iexpect a higher appeal in this English edition. I am very much obliged to Dr. Thomas Ditzinger, Engineering Editor, Springer-Verlag, for his continuous help in publication. Tokyo, Japan Akira Hirose April 2006 Preface The studies on complex-valued neural networks have recently been evolving in various directions. The pioneering areas include electromagnetic-wave and lightwave sensing and imaging, independent component analysis in blind s- aration, blur restoration in image processing, and so on. Developing appli- tionsinvolveadaptivequantumdevices,quantumcomputation,socialsystems related to periodicity and oscillation, and so forth. Swarm intelligence is an innovative computational way to solving hard pr- lems. This discipline is inspired by the behavior of social insects such as?sh schools and bird?ocks and colonies of ants, termites, bees and wasps. In g- eral, this is done by mimicking the behavior of the biological creatures within their swarms and colonies. Particle swarm optimization, also commonly known as PSO, mimics the behaviorofaswarmofinsectsoraschoolof?sh.Ifoneoftheparticlediscovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensionalspacethathavetwocharacteristics:apositionandavelocity. Theseparticleswanderaroundthehyperspaceandrememberthebestposition that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions. The ant colony optimization, commonly known as ACO, is a probabilistic technique for solving computational hard problems which can be reduced to?ndingoptimalpaths.ACOisinspiredbythebehaviorofantsin?ndingshort paths from the colony nest to the food place. Ants have small brains and bad vision yet they use great search strategy. Initially, real ants wander randomly to?nd food. They return to their colony while laying down pheromone trails. If other ants?nd such a path, they are likely to follow the trail with some pheromone and deposit more pheromone if they eventually?nd food. This book is the first monograph ever on complex-valued neural networks, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. It is useful for those beginning their studies, for instance, adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, brainlike information processing, robotics inspired by human neural systems, and interdisciplinary studies to realize comfortable society. It is also helpful to those who carry out research and development regarding new products and services at companies. The author wrote this book hoping in particular that it provides the readers with meaningful hints to make good use of neural networks in fully practical applications. The book emphasizes basic ideas and ways of thinking. Why do we need to consider neural networks that deal with complex numbers? What advantages do the complex-valued neural networks have? What is the origin of the advantages? In what areas do they develop principal applications? This book answers these questions by describing details and examples, which will inspire the readers with new ideas

data Mining Analyzes Large Amounts Of Data To Discover Knowledge Relevant To Decision Making. Typically, Numerous Pieces Of Knowledge Are Extracted By A Data Mining System And Presented To A Human User, Who May Be A Decision-maker Or A Data-analyst. The User Is Confronted With The Task Of Selecting The Pieces Of Knowledge That Are Of The Highest Quality Or Interest According To His Or Her Preferences. Since This Selection Is Sometimes A Daunting Task, Designing Quality And Interestingness Measures Has Become An Important Challenge For Data Mining Researchers In The Last Decade.

this Volume Presents The State Of The Art Concerning Quality And Interestingness Measures For Data Mining. The Book Summarizes Recent Developments And Presents Original Research On This Topic. The Chapters Include Surveys, Comparative Studies Of Existing Measures, Proposals Of New Measures, Simulations, And Case Studies. Both Theoretical And Applied Chapters Are Included. Papers For This Book Were Selected And Reviewed For Correctness And Completeness By An International Review Committee.

This volume offers a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems as well as applications and interesting experiences using the particle swarm optimisation. Swarm intelligence is an innovative computational way to solve hard problems. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions This monograph instructs graduate- and undergraduate-level students in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering on the concepts of complex-valued neural networks. Emphasizing basic concepts and ways of thinking about neural networks, the author focuses on neural networks that deal with complex numbers; the practical advantages of complex-valued neural networks, and their origins; the development of principal applications? The book uses detailed examples to answer these questions and more. Systems Designers Have Learned That Many Agents Co-operating Within The System Can Solve Very Complex Problems With A Minimal Design Effort. In General, Multi-agent Systems That Use Swarm Intelligence Are Said To Be Swarm Intelligent Systems. Today, These Are Mostly Used As Search Engines And Optimization Tools. This Volume Reviews Innovative Methodologies Of Swarm Intelligence, Outlines The Foundations Of Engineering Swarm Intelligent Systems And Applications, And Relates Experiences Using The Particle Swarm Optimisation. The art of the artificial neural networks is a technological framework in which we introduce and/or imitate the functions, constructions, and dynamics of the brain to realize an adaptive and useful information processing. This book presents recent advances in quality measures in data mining.