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.

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.

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.

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.

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.

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.

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.