[PDF] Advances In K Means Clustering eBook

Author : Junjie Wu
Publisher : Springer Science & Business Media
Page : 187 pages
File Size : 16,87 MB
Release : 2012-07-09
Category : Computers
ISBN : 3642298079

GET BOOK

Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.

Author : Hector Geffner
Publisher : Springer
Page : 476 pages
File Size : 12,11 MB
Release : 2008-10-01
Category : Computers
ISBN : 3540883096

GET BOOK

IBERAMIA is the international conference series of the Ibero-American Art- cialIntelligencecommunitythathasbeenmeetingeverytwoyearssincethe1988 meeting in Barcelona. The conference is supported by the main Ibero-American societies of AI and provides researchers from Portugal, Spain, and Latin Am- ica the opportunity to meet with AI researchers from all over the world. Since 1998, IBERAMIA has been a widely recognized international conference, with its papers written and presented in English, and its proceedings published by Springer in the LNAI series. This volume contains the papers accepted for presentation at Iberamia 2008, held in Lisbon, Portugal in October 2008. For this conference, 147 papers were submitted for the main track, and 46 papers were accepted. Each submitted paper was reviewed by three members of the Program Committee (PC), coor- nated by an Area Chair. In certain cases, extra reviewerswererecruited to write additional reviews. The list of Area Chairs, PC members, and reviewers can be found on the pages that follow. The authors of the submitted papers represent 14 countries with topics c- ering the whole spectrum of themes in AI: robotics and multiagent systems, knowledge representation and constraints, machine learning and planning, n- ural language processing and AI applications. TheprogramforIberamia2008alsoincludedthreeinvitedspeakers:Christian Lemaitre (LANIA, M ́ exico), R. Michael Young (NCSU, USA) and Miguel Dias (Microsoft LDMC, Lisbon) as well as ?ve workshops.

Author : Sugato Basu
Publisher : CRC Press
Page : 472 pages
File Size : 18,31 MB
Release : 2008-08-18
Category : Computers
ISBN : 9781584889977

GET BOOK

Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the theoretical properties of constraints and constrained clustering algorithms. Bringing these developments together, Constrained Clustering: Advances in Algorithms, Theory, and Applications presents an extensive collection of the latest innovations in clustering data analysis methods that use background knowledge encoded as constraints. Algorithms The first five chapters of this volume investigate advances in the use of instance-level, pairwise constraints for partitional and hierarchical clustering. The book then explores other types of constraints for clustering, including cluster size balancing, minimum cluster size,and cluster-level relational constraints. Theory It also describes variations of the traditional clustering under constraints problem as well as approximation algorithms with helpful performance guarantees. Applications The book ends by applying clustering with constraints to relational data, privacy-preserving data publishing, and video surveillance data. It discusses an interactive visual clustering approach, a distance metric learning approach, existential constraints, and automatically generated constraints. With contributions from industrial researchers and leading academic experts who pioneered the field, this volume delivers thorough coverage of the capabilities and limitations of constrained clustering methods as well as introduces new types of constraints and clustering algorithms.

Author : Vinu Das
Publisher : Springer Science & Business Media
Page : 900 pages
File Size : 27,87 MB
Release : 2013-01-02
Category : Computers
ISBN : 364225733X

GET BOOK

This book constitutes the proceedings of the First International Conference on Computational Intelligence and Information Technology, CIIT 2011, held in Pune, India, in November 2011. The 58 revised full papers, 67 revised short papers, and 32 poster papers presented were carefully reviewed and selected from 483 initial submissions. The papers are contributed by innovative academics and industrial experts in the field of computer science, information technology, computational engineering, mobile communication and security and offer a stage to a common forum, where a constructive dialog on theoretical concepts, practical ideas and results of the state of the art can be developed.

Author : Jacob Kogan
Publisher : Springer Science & Business Media
Page : 273 pages
File Size : 11,2 MB
Release : 2006-02-08
Category : Computers
ISBN : 3540283498

GET BOOK

Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.

Author : Michael R. Berthold
Publisher : Springer
Page : 588 pages
File Size : 17,76 MB
Release : 2020-04-02
Category : Computers
ISBN : 9783030445836

GET BOOK

This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.

Author : Viet-Ha Nguyen
Publisher : Springer
Page : 673 pages
File Size : 38,89 MB
Release : 2014-09-29
Category : Technology & Engineering
ISBN : 3319116800

GET BOOK

This volume contains papers presented at the Sixth International Conference on Knowledge and Systems Engineering (KSE 2014), which was held in Hanoi, Vietnam, during 9–11 October, 2014. The conference was organized by the University of Engineering and Technology, Vietnam National University, Hanoi. Besides the main track of contributed papers, this proceedings feature the results of four special sessions focusing on specific topics of interest and three invited keynote speeches. The book gathers a total of 51 carefully reviewed papers describing recent advances and development on various topics including knowledge discovery and data mining, natural language processing, expert systems, intelligent decision making, computational biology, computational modeling, optimization algorithms, and industrial applications.

Author : Krzystof Jajuga
Publisher : Springer Science & Business Media
Page : 468 pages
File Size : 43,18 MB
Release : 2012-12-06
Category : Computers
ISBN : 3642561810

GET BOOK

The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.

Author : Paula Brito
Publisher : Springer Science & Business Media
Page : 619 pages
File Size : 21,18 MB
Release : 2007-08-16
Category : Mathematics
ISBN : 3540735607

GET BOOK

This volume presents recent methodological developments in data analysis and classification. It covers a wide range of topics, including methods for classification and clustering, dissimilarity analysis, consensus methods, conceptual analysis of data, and data mining and knowledge discovery in databases. The book also presents a wide variety of applications, in fields such as biology, micro-array analysis, cyber traffic, and bank fraud detection.

Author : Sourav De
Publisher : John Wiley & Sons
Page : 196 pages
File Size : 36,36 MB
Release : 2020-06-02
Category : Computers
ISBN : 1119551609

GET BOOK

An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors noted experts on the topic provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.