Music recommendation systems are becoming more and more popular. The increasing amount of personal data left by users on social media contributes to more accurate inference of the user's musical preferences and the same to quality of personalized systems. Health recommendation systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy concerns. Medical doctors deal with various kinds of diseases in which the music therapy helps to improve symptoms. Listening to music may improve heart rate, respiratory rate, and blood pressure in people with heart disease. Sound healing therapy uses aspects of music to improve physical and emotional health and well-being. The book presents a variety of approaches useful to create recommendation systems in healthcare, music, and in music therapy.

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.

This book constitutes the refereed proceedings of the International Conference on Brain Informatics, BI 2012, held in Macau, China, in December 2012. The 34 revised full papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on computer science, information technology, artificial intelligence, web intelligence, cognitive science, neuroscience, medical science, life science, economics, data mining, data and knowledge engineering, intelligent agent technology, human computer interaction, complex systems, and system science.

Rough set approach to reasoning under uncertainty is based on inducing knowledge representation from data under constraints expressed by discernibility or, more generally, similarity of objects. Knowledge derived by this approach consists of reducts, decision or association rules, dependencies, templates, or classifiers. This monograph presents the state of the art of this area. The reader will find here a deep theoretical discussion of relevant notions and ideas as well as rich inventory of algorithmic and heuristic tools for knowledge discovery by rough set methods. An extensive bibliography will help the reader to get an acquaintance with this rapidly growing area of research.

After 20 years of pursuing rough set theory and its applications a look on its present state and further prospects is badly needed. The monograph Rough Set Theory and Granular Computing edited by Masahiro Inuiguchi, Shoji Hirano and Shusaku Tsumoto meets this demand. It presents the newest developments in this area and gives fair picture of the state of the art in this domain. Firstly, in the keynote papers by Zdzislaw Pawlak, Andrzej Skowron and Sankar K. Pal the relationship of rough sets with other important methods of data analysis -Bayes theorem, neuro computing and pattern recognitio- is thoroughly examined. Next, several interesting generalizations of the the ory and new directions of research are presented. Furthermore application of rough sets in data mining, in particular, rule induction methods based on rough set theory is presented and discussed. Further important issue dis cussed in the monograph is rough set based data analysis, including study of decisions making in conflict situations. Last but not least, some recent engi neering applications of rough set theory are given. They include a proposal of rough set processor architecture organization for fast implementation of ba sic rough set operations and discussion of results concerning advanced image processing for unmanned aerial vehicle. Thus the monograph beside presenting wide spectrum of ongoing research in this area also points out new emerging areas of study and applications, which makes it a valuable source of information to all interested in this do main."

This book collects selected papers by authors for CODATA 2006, which are relevant to the acquisition of knowledge and the assessment of risk and opportunity that comes from combining data from a number of different disciplines.

For this book, the editors invited contributions from indispensable research areas relevant to "chance discovery," which has been defined as the discovery of events significant for making a decision, and studied since 2000. The chapters contain contributions to identifying rare or hidden events and explaining their significance. The methods presented in this book are based on the interaction of human, machine, and humans living environment.

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

This book constitutes the refereed proceedings of the Fourth International Conference on Rough Sets and Knowledge Technology, RSKT 2009, held in Gold Coast, Australia, in July 2009. The 85 revised full papers presented together with 3 keynote papers and 2 special sessions were carefully reviewed and selected from 229 submissions. The papers are organized in topical sections on rough sets and computing, rough sets and data reduction, data mining and knowledge discovery, granular computing and cognitive computing, fuzzy sets and computing, knowledge technology and intelligent systems, computational intelligence and applications, image processing and understanding, and formal concept analysis.

Asdataminingtechniquesandtoolsmature, theirapplicationdomainsextendto previousuncharteredterritories.The commontheme ofthe workshopsorganized along with the main 2008 Paci?c Asia Conference on Knowledge Discovery and Data Mining (PAKDD) in Osaka, Japan was to extend the application of data mining techniques to new frontiers. Thus the title of the proceedings: "New Frontiers in Application of Data Mining." For the 2008 program, three workshops were organized. 1. Algorithms for Large-Scale Information Processing (ALSIP). The focus of the workshop was novel algorithms and data structures to deal with p- cessing of very large data sets. 2. Data Mining for Decision Making and Risk Management (DMDRM), which emphasized applications of risk information derived from data mining te- niques on diverse applications ranging from medicine to marketing to chemistry. 3. Interactive Data Mining (IDM), which emphasized the relationship between techniques in data mining and human-computer interaction. In total 38 papers were submitted to the workshops. After consultation with theworkshopChairswhowereaskedto ranktheir submissions,18wereaccepted for publicationin this volume.We hope that the published papers propelfurther interest in the growing ?eld of knowledge discovery in databases (KDD). The paper selection of the industrial track and the workshops was made by the Program Committee of each organization. Upon the paper selection, the book was edited and managed by the volume editors.

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

This book collects selected papers by authors for CODATA 2006, which are relevant to the acquisition of knowledge and the assessment of risk and opportunity that comes from combining data from a number of different disciplines.

This volume contains 20 papers selected for presentation at the Third Inter- tional Workshopon Mining Complex Data-MCD2007-held in Warsaw, Poland, September 17-21, 2007. MCD is a workshop series that started in conjunction with the 5th IEEE International Conference on Data Mining (ICDM) in Ho- ton, Texas, November27-30,2005.ThesecondMCDworkshopwasheldagainin conjunction with the ICDM Conference in Hong Kong, December 18-22, 2006. Data mining and knowledge discovery, as stated in their early de?nition, can today be considered as stable ?elds with numerous e?cient methods and studies that have been proposed to extract knowledge from data. Nevertheless, the famous golden nugget is still challenging. Actually, the context evolved since the ?rst de?nition of the KDD process, and knowledge now has to be extracted from data becoming more and more complex. In the frameworkof data mining, many softwaresolutions were developedfor theextractionofknowledgefromtabulardata(whicharetypicallyobtainedfrom relationaldatabases).Methodologicalextensionswereproposedtodealwithdata initiallyobtainedfromothersources, e.g., inthecontextofnaturallanguage(text mining) and image (image mining). KDD has thus evolved following a unimodal scheme instantiated according to the type of the underlying data (tabular data, text, images, etc.), which, at the end, always leads to working on the classical double entry tabular format. However, in a large number of application domains, this unimodal approach appears to be too restrictive. Consider for instance a corpus of medical

This book constitutes the thoroughly refereed post-proceedings of the 2006 Pacific Rim Knowledge Acquisition Workshop, PKAW 2006, held in Guilin, China in August 2006 as part of 9th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006.

The 21 revised full papers and 6 revised short papers presented together with 4 invited talks were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on ontology and knowledge acquisition, algorithm approaches to knowledge acquisition, incremental knowledge acquisition and RDR, knowledge acquisition and applications, as well as machine learning and data mining.

For this book, the editors invited contributions from indispensable research areas relevant to "chance discovery," which has been defined as the discovery of events significant for making a decision, and studied since 2000. The chapters contain contributions to identifying rare or hidden events and explaining their significance. The methods presented in this book are based on the interaction of human, machine, and humans living environment.

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.

This volume contains the papers selected for presentation at the 2nd Inter- tional Workshop on Active Mining (AM 2003) which was organized in conju- tion with the 14th International Symposium on Methodologies for Intelligent Systems (ISMIS 2003), held in Maebashi City, Japan, 28-31 October, 2003. The workshop was organized by the Maebashi Institute of Technology in - operation with the Japanese Society for Arti?cial Intelligence. It was sponsored by the Maebashi Institute of Technology, the Maebashi Convention Bureau, the Maebashi City Government, the Gunma Prefecture Government, JSAI SIGKBS (Japanese Arti?cial Intelligence Society, Special Interest Group on Knowledge- Based Systems), a Grant-in-Aid for Scienti?c Research on Priority Areas (No. 759) "Implementation of Active Mining in the Era of Information Flood," US AFOSR/AOARD, the Web Intelligence Consortium (Japan), the Gunma Inf- mation Service Industry Association, and Ryomo Systems Co., Ltd. ISMIS is a conference series that was started in 1986 in Knoxville, Tennessee. SincethenithasbeenheldinCharlotte(NorthCarolina), Knoxville(Tennessee), Torin (Italy), Trondheim (Norway), Warsaw (Poland), Zakopane (Poland), and Lyon (France). The objective of this workshop was to gather researchers as well as prac- tioners who are working on various research ?elds of active mining, share ha- learned experiences, and shed light on the future development of active mining.

This volume contains the papers selected for presentation at the 15th International S- posiumonMethodologiesforIntelligentSystems, ISMIS2005, heldinSaratogaSprings, NewYork,25 28May,2005. ThesymposiumwasorganizedbySUNYatAlbany. Itwas sponsored by the Army Research Of?ce and by several units of the University at Albany including its Division for Research, College of Arts and Sciences, Department of C- puter Science, and Institute for Informatics, Logics, and Security Studies (formerly the Institute for Programming and Logics). ISMIS is a conference series that was started in 1986 in Knoxville, Tennessee. Since then it has been held in Charlotte (North Carolina), Knoxville(Tennessee), Turin(Italy), Trondheim(Norway), Warsaw(Poland), Zakopane (Poland), Lyon (France), and Maebashi City (Japan). The Program Committee selected the following major areas for ISMIS 2005: intelligent information systems, knowledge discovery and data mining, knowledge - formation and integration, knowledge representation, logic for arti?cial intelligence, soft computing, Web intelligence, Web services, and papers dealing with applications of intelligent systems in complex/novel domains. The contributed papers were selected from almost 200 full draft papers by the Program Committee members: Troels Andreasen, Peter Baumgartner, Boualemn Benatallah, Salima Benbernou, Veronique Benzaken, Petr Berka, Elisa Bertino, Alan Biermann, Jacques Calmet, Sandra Carberry, Juan Carlos Cubero, Luigia Carlucci Aiello, Shu-Ching Chen, Christine Collet, Agnieszka Dardzinska, Ian Davidson, Robert Demolombe, Jitender Deogun, Jon Doyle, Tapio Elomaa, Attilio Giordana, Jerzy Grzymala-Busse, MirsadHadzikadic, ReinerHaehnle, JanuszKacprzyk, VipulKashyap, Jan Komorowski, Jacek Koronacki, Tsau Young Lin, Donato Malerba, David Maluf, Davide Martinenghi, Stan Matwin, Natasha Noy, Werner Nutt, James Peters, Jean-Marc Petit, Vijay Raghavan, Jan Rauch, Gilbert Ritschard, Erik Rosenthal, Marie-Christine Rousset, Nahid Shahmehri, Andrzej Skowron, Dominik Slezak, Nicolas Spyratos

In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learning, and approximate reasoning. In particular, rough sets are used in probabilistic reasoning, granular computing (including information granule calculi based on rough mereology), intelligent control, intelligent agent modeling, identi?cation of autonomous s- tems, and process speci?cation. Methods based on rough set theory alone or in combination with other - proacheshavebeendiscoveredwith awide rangeofapplicationsinsuchareasas: acoustics, bioinformatics, business and ?nance, chemistry, computer engineering (e.g., data compression, digital image processing, digital signal processing, p- allel and distributed computer systems, sensor fusion, fractal engineering), de- sion analysis and systems, economics, electrical engineering (e.g., control, signal analysis, power systems), environmental studies, informatics, medicine, mole- lar biology, musicology, neurology, robotics, social science, software engineering, spatial visualization, Web engineering, and Web mining.

This volume contains the papers selected for presentation at the 14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003, held in Maebashi City, Japan, 28-31 October, 2003. The symposium was organized by the Maebashi Institute of Technology in co-operation with the Japanese Society for Artificial Intelligence. It was sponsored by the Maebashi Institute of Technology, Maebashi Convention Bureau, Maebashi City Government, Gunma Prefecture Government, US AFOSR/AOARD, the Web Intelligence Consortium (Japan), Gunma Information Service Industry Association, and Ryomo Systems Co., Ltd. ISMIS is a conference series that was started in 1986 in Knoxville, Tennessee. Since then it has been held in Charlotte (North Carolina), Knoxville (Tennessee), Turin (Italy), Trondheim (Norway), Warsaw (Poland), Zakopane (Poland), and Lyon (France). The program committee selected the following major areas for ISMIS 2003: active media human-computer interaction, autonomic and evolutionary computation, intelligent agent technology, intelligent information retrieval, intelligent information systems, knowledge representation and integration, knowledge discovery and data mining, logic for artificial intelligence, soft computing, and Web intelligence.

This monograph presents novel approaches and new results in fundamentals and applications related to rough sets and granular computing. It includes the application of rough sets to real world problems, such as data mining, decision support and sensor fusion. The relationship of rough sets to other important methods of data analysis – Bayes theorem, neurocomputing and pattern recognition is thoroughly examined. Another issue is the rough set based data analysis, including the study of decision making in conflict situations. Recent engineering applications of rough set theory are given, including a processor architecture organization for fast implementation of basic rough set operations and results concerning advanced image processing for unmanned aerial vehicles. New emerging areas of study and applications are presented as well as a wide spectrum of on-going research, which makes the book valuable to all interested in the field of rough set theory and granular computing.