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Ajith abraham Crina groan Vitorino Ramos (eds) Swarm Intelligence in data mining With 91 Figures and 73 Tables pringer Dr. Ajith abraham Dr Vitorino ramos IITA Professorship program CVRM-IST IST School of Computer Science Technical University of I isbon ngineering Chung-Ang University, 221 1049-001, Lisboa Heukseok-dong Portugal Dongjak-gu Seoul 156-756 E-mail: vitorino. ramos@alfa ist utl. pt Republic of Korea E-mail: ajith. abraham@ieee. org abraham.ajith@acm.org Dr Crina groan Department of Computer Science faculty of mathematics and Computer sci Babes-Bolyai University Cluj-Napoca, Kogalniceanu 1 400084 Cluj-Napoca Romania E-mail: cgrosan@cs. ubbclujro Library of Congress Control Number: 2006928619 Issn Print edition: 1860-949X ISSN electronic edition: 1860-9503 IsBN-10 3-540-34955-3 Springer Berlin Heidelberg New York ISBN-13 978-3-540-34955-6 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the mate rial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recital tion, broadcasting, reproduction on microfilm or in any other way, and storage in data bank Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use Ist always be obtained fromi Springer-Verlag. Violations are liable to prosecution under u German Copyright Law Springer is a part of Springer Science+ Business Media springer. com O Springer-Verlag Berlin Heidelberg 2006 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specitic statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Typesetting by the authors and spi Printed on acid-free paper SPIN: 11613589 89/SP 543210 Foreword Science is a swarm To the layperson, the stereotypical scientist is logical, clear-thinking, well informed but perhaps socially awkward, carefully planning his or her experiments and then analyzing the resulting data deliberately, with precision. The scientist works alone, emotion-free, searching only for truth, having been well advised about the pitfalls and temptations that lie along the path to discovery and the expansion of human knowledge Those who work in science understand how inaccurate this stereotype is. In eality, researchers' daily routines follow a process better described as collective trial-and-error, nearly random at times. A most salient feature of scientific behavior is its collaborative nature. From applying for grants to seeking tenure, from literature reviews to peer review to conference presentations, every bit of the scientific enterprise is social, every step of the process is designed to make scientists aware of one another's work, to force researchers to compare, to communicate, to study the work that others are doing, in order to push the paradigm forward-not as independent, isolated seekers-of-truth, but more like a swarm If we plotted a group of scientists as points on a space of dimensions of theories and methods, and ran the plot so we could see changes over time, we would see individuals colliding and crossing, escaping the groups gravity field and returning, disintegrating but simultaneously cohering in some mysterious way and moving as a deliberate, purposeful bunch, across the space -constantly pushing toward a direction that improves the state of knowledge, sometimes stepping in the wrong direction, but relentlessly insisting toward an epistemological optimum The book you hold in your hand is a snapshot of the swarm that is the swarm paradigm, a flash photograph of work by researchers from all over the world, captured in mid-buzz as they search, using collective trial and error, for ways to take advantage of processes that are observed in nature and instantiated in computer programs In this volume you will read about a nuinber of different kinds of computer programs that are called"swarms. " It really wouldnt be right for something as messy as a swarm to have a crisp, precise definition. In general the word swarm Foreword is probably more connotative than denotative; there is more to the way swarms feel than to any actual properties that may characterize them. a swarm is going to have sOme randomness in it-it will not be perfectly choreographed like a flock or a school A swarm is going to contain a good number of members. The members of the swarm will interact with one another in some way, that is. they will affect one anothers behaviors. As they influence one another, there will be some order and some chaos in the population. This is what a swarm is The swarm intelligence literature has mostly arisen around two families of algorithms. One kind develops knowledge about a problem by the accumu lation of artifacts, often metaphorically conceptualized as pheromones. Individuals respond lo signs of their peers behaviors, leaving signs themselves; those signs increase or decay depending, in the long run, on how successfully they indicate a good solution for a given problem. The movements of swarm population members are probabilistically chosen as a function of the accumulation of pheromone along a de In another kind of swarm algorithm each individual is a candidate problem solution: in the beginning the solutions are random and not very good but they improve over time. Individuals interact directly with their peers, emulating their successes; each individual serves as both teacher and learner and in the end the researcher can interrogate the most successful member of the population to find, usually, a good problem solution It nportant that both of these kinds of algorithms ant colony swarms and the particle swarms, are included together in one volume, along with other kinds of swarms. In the forward push of knowledge it is useful for researchers to look over and see what the others are doing; the swarm of science works through the integration of disparate points of view. Already we are seeing papers describing hybrids of these approaches, as well as other evolutionary and heuristic methods -this is an inevitable and healthy direction for the research to take. Add to this the emergence of new swarm methods, based for instance on honey bee behaviors, and you see in this volume the upward trajectory of a rich, blooming new field of research Science is a way of searching, and should not be mistaken for a list of answers it is a fountain of questions, and the pursuit of answers. No chapter in this book or any other will give you the full, final explanation about how swarms learn, optimize and solve problems; every chapter will give you insights into how the unpredictable and messy process of swarming can accomplish these things As the stereotype of the scientist as a lone intellect has been challenged, revising the stereotype should change the way we think about knowledge, as well. Knowledge is not a package of information stored in a brain, it is a process distributed across many brains. Knowing is something that only living beings can do, and knowing in the scientific sense only takes place when individuals participate in the game Every paradigm has its leaders and its followers, its innovators and its drones, but no scientific paradigm can exist without communication and all the living behaviors that go with that- collaboration, competition, conflict, collision, coordination, caring These chapters are technical and challenging, and rewarding. here our basic task is data-mining, where we have some information and want to make sense Foreword of it, however we have defined that. Swarm methods are generally good in high dimensions, with lots of variables; they tend to be robust in noisy spaces; swarms are unafraid of multinodal landscapes, with lots of good-but-not-best solutions Researchers in this volume are pushing this new paradigm into highly demanding data sets, reporting here what they are able to get it to do May05,2006 James Kennedy, USA Preface Swarm Intelligence (SI) 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. Particle Swarm Optimization(PSO) incorporates swarming behaviors ohserved in flocks of birds. schools of fish or swarms of bees and even human social behavior from which the idea is emerged. Ant Colony Optimization(ACO) deals with artificial systems that is inspired from the foraging behavior of real ants, which are used to solve discrete optimization problems Historically the notion of finding useful patterns in data has been given a variety of names including data mining, know ledge discovery, information extraction, etc 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 In order to achieve this, data mining uses computational techniques from statistics machine learning and pattern recognition Data mining and Swarm intelligence may seem that they do not have many properties in common. However, recent studies suggests that they can be used together for several real world data mining problems especially when other methods would be too expensive or difficult to implement This book deals with the application of swarm intelligence methodologies 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 1 l chapters including an introductory chapters giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. The eleven chapters are organized as follows In Chapter 1, Grosan et al present the biological motivation and some of the theoretical concepts of swarm intelligence with an emphasis on particle swarm optimization and ant colony optimization algorithIns. The basic data mining terminologies are explained and linked with some of the past and ongoing works using swarm intelligence techniques Preface Martens et al. in Chapter 2 introduce a new algorithm for classification named AntMiner+, based on an artificial ant system with inherent self- organizing capabilities. AntMiner+ differs from the previously proposed AntMiner classification technique in three aspects. Firstly, AntMiner+ uses a MAX-MIN ant system which is an improved version of the originally proposed ant system, yielding better performing classifiers. Secondly, the complexity of the environment in which the ants operate has substantially decreased. Finally, AntMiner+ leads to fewer and better performing rules In Chapter 3, Jensen presents a feature selection mechanism based on ant colony optimization algorithm to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. The proposed method is applied to two very different challenging tasks, namely web classification and complex systems monitoring Galea and shen in the fourth chapter present an ant colony optimization approach for the induction of fuzzy rules. Several ant colony optimization algorithms are run simultaneously, with each focusing on finding descriptive rules for a specific class The final outcome is a fuzzy rulebase that has been evolved so that individual rules complement each other during the classification process In the fifth chapter Tsang and Kwong present an ant colony based clustering model for intrusion detection. The proposed model improves existing ant-based clustering algorithms by incorporating some meta-heuristic principles. To further improve the clustering solution and alleviate the curse of dimensionality in network connection data, four unsupervised feature extraction algorithms are also studied and evaluated Omran et al. in the sixth chapter present particle swarm optimization algorithms for pattern recognition and image processing problems. First a clustering method that is based on PSo is discussed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. Then PSo-based approaches that tackle the color image quantization and spectral unmixing problems are discussed In the seventh chapter Azzag et al. present a new model for data clustering which is inspired from the self-assembly behavior of real ants. Real ants can build complex structures by connecting themselves to each others. It is shown is th that this behavior can be used to build a hierarchical tree-structured partitioning of the data according to the similarities between those data. Authors have also introduced an incremental version of the artificial ants algorithm Kazemian et al. in the eighth chapter presents a new swarm data clustering method based on Flowers Pollination by Artificial Bees(FPAB. FPAB does not require any parameter settings and any initial information such as the number of classes and the number of partitions on input data. Initially, in FPAB, bees move the pollens and pollinate them. Each pollen will grow in proportion to its garden flowers Better growing will occur in better conditions. After some iterations, natural selection reduces the pollens and flowers and the gardens of the same type of flowers will be formed. The prototypes of each gardens are taken as the initial cluster centers for Fuzzy C Means algorithm which is used to reduce ohvious misclassification Preface errors In the next stage, the prototypes of gardens are assumed as a single flower and FPAB is applied to them again Palotaiet al in the ninth chapter propose an Alife architecture for news foraging News foragers in the Internet were evolved by a simple internal selective algorithm selection concerned the memory components, being finite in size and containing the list of most promising supplies. Foragers received reward for locating not yet found news and crawled by using value estimation Foragers were allowed to multiply if they passed a given productivity threshold. a particular property of this community is that there is no direct interaction(here, communication)amongst foragers that allowed us to study compartmentalization, assumed to be important for scalability in a very clear form Veenhuis and Koppen in the tenth chapter introduce a data clustering algorithm based on species clustering. It combines methods of particle swarm optimization and flock algorithms. A given set of data is interpreted as a multi-species swarm which wants to separate into single-species swarms, 1.e., clusters. The data to be clustered are assigned to datoids which form a swarm on a two-dimensional plane. A datoid can be imagined as a bird carrying a piece of data on its back. While swarming, this swarn divides into sub-swarms moving over the plane and consisting of datoids carrying similar data. After swarming, these sub swarms of datoids can be grouped together as clusters In the last chapter Yang et al. present a clustering ensemble model using ant colony algorithm with validity index and art neural network. Clusterings are visually formed on the plane by ants walking, picking up or dropping down projected data objects with different probabilities. Adaptive resonance Theory (ART) is employed to combine the clusterings produced by ant colonies with different movin speeds We are very much grateful to the authors of this volume and to the reviewers for their tremendous service by critically reviewing the chapters. The editors would like to thank Dr. Thomas Ditzinger(Springer Engineering Inhouse Editor, Studies in Computational Intelligence Series), Professor Janusz Kacprzyk Editor-in-Chief Springer Studies in Computational Intelligence Series) and Ms. Heather Kin (Editorial Assistant, Springer Verlag, Heidelberg) for the editorial assistance and excellent cooperative collaboration to produce this important scientific work. We hope that the reader will share our excitement to present this volume on 'Swarm Intelligence in data mining and will find it useful April, 2006 Ajith Abraham, Chung-Ang University, Seoul, Korec Crinu Grosan, Cluj-Napoca, Bubey-Bolyui University, Romania Vitorino ramos, Technical Universiry of lisbon, Portugal

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