Modern knowledge discovery methods enable users to discover complex patterns of various types in large information repositories. However, the underlying assumption has always been that the data to which the methods are applied to originates from one domain. The focus of this book, and the BISON project from which the contributions are originating, is a network based integration of various types of data repositories and the development of new ways to analyse and explore the resulting gigantic information networks. Instead of finding well defined global or local patterns they wanted to find domain bridging associations which are, by definition, not well defined since they will be especially interesting if they are sparse and have not been encountered before. The 32 contributions presented in this state-of-the-art volume together with a detailed introduction to the book are organized in topical sections on bisociation; representation and network creation; network analysis; exploration; and applications and evaluation.

Computational creativity is an emerging field of research within AI that focuses on the capacity of machines to both generate and evaluate novel outputs that would, if produced by a human, be considered creative. This book is intended to be a canonical text for this new discipline, through which researchers and students can absorb the philosophy of the field and learn its methods. After a comprehensive introduction to the idea of systematizing creativity the contributions address topics such as autonomous intentionality, conceptual blending, literature mining, computational design, models of novelty, evaluating progress in related research, computer-supported human creativity and human-supported computer creativity, common-sense knowledge, and models of social creativity. Products of this research will have real consequences for the worlds of entertainment, culture, science, education, design, and art, in addition to artificial intelligence, and the book will be of value to practitioners and students in all these domains.

Modern knowledge discovery methods enable users to discover complex patterns of various types in large information repositories. However, the underlying assumption has always been that the data to which the methods are applied to originates from one domain. The focus of this book, and the BISON project from which the contributions are originating, is a network based integration of various types of data repositories and the development of new ways to analyse and explore the resulting gigantic information networks. Instead of finding well defined global or local patterns they wanted to find domain bridging associations which are, by definition, not well defined since they will be especially interesting if they are sparse and have not been encountered before. The 32 contributions presented in this state-of-the-art volume together with a detailed introduction to the book are organized in topical sections on bisociation; representation and network creation; network analysis; exploration; and applications and evaluation.

This book constitutes the refereed proceedings of the Third Workshop on Human-Computer Interaction and Knowledge Discovery, HCI-KDD 2013, held in Maribor, Slovenia, in July 2013, at SouthCHI 2013. The 20 revised papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in topical sections on human-computer interaction and knowledge discovery, knowledge discovery and smart homes, smart learning environments, and visualization data analytics.

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. PAKDD 2008, the 12th in the series, was heldatOsaka,JapanduringMay20–23,2008.PAKDDisaleadinginternational conference in the area of data mining. It provides an international forum for - searchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD-related areas - cluding data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. This year we received a total of 312 research papers from 34 countries and regions in Asia, Australia, North America, South America, Europe, and Africa. Every submitted paper was rigorously reviewed by two or three reviewers, d- cussed by the reviewers under the supervision of an Area Chair, and judged by the Program Committee Chairs. When there was a disagreement, the Area Chair and/or the Program Committee Chairs provided an additional review. Thus, many submissions were reviewed by four experts. The Program Comm- tee members were deeply involved in a highly selective process. As a result, only approximately11.9%ofthe312submissionswereacceptedaslongpapers,12.8% of them were accepted as regular papers, and 11.5% of them were accepted as short papers.

This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Analysis, IDA 2011, held in Porto, Portugal, in October 2011. The 19 revised full papers and 16 revised poster papers resented together with 3 invited papers were carefully reviewed and selected from 73 submissions. All current aspects of intelligent data analysis are addressed, particularly intelligent support for modeling and analyzing complex, dynamical systems. The papers offer intelligent support for understanding evolving scientific and social systems including data collection and acquisition, such as crowd sourcing; data cleaning, semantics and markup; searching for data and assembling datasets from multiple sources; data processing, including workflows, mixed-initiative data analysis, and planning; data and information fusion; incremental, mixed-initiative model development, testing and revision; and visualization and dissemination of results; etc.

This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.