This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.

This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.

This book constitutes the proceedings of the 12th IFIP TC 8 International Conference, CISIM 2013, held in Cracow, Poland, in September 2013. The 44 papers presented in this volume were carefully reviewed and selected from over 60 submissions. They are organized in topical sections on biometric and biomedical applications; pattern recognition and image processing; various aspects of computer security, networking, algorithms, and industrial applications. The book also contains full papers of a keynote speech and the invited talk.

This book constitutes the refereed proceedings of the International Conference on Intelligent Information Systems, IIS 2013, held in Warsaw, Poland in June 2013. The 28 full papers included in this volume were carefully reviewed and selected from 53 submissions. The contributions are organized in topical sections named: Natural language processing, text and Web mining, and machine learning and search.

This book constitutes the refereed proceedings of the 11th International Conference on Computer Information Systems and Industrial Management, CISIM 2012, held in Venice, Italy, in September 2012. The 35 revised full papers presented together with 2 keynote talks were carefully reviewed and selected from 80 submissions. The papers are organized in topical sections on security, access control and intrusion detection; pattern recognition and image processing; biometric applications; algorithms and data management; networking; and system models and risk assessment.

This volume contains articles accepted for presentation during The Intelligent Information Systems Symposium IIS'2002 which was held in Sopot, Poland, on June 3-6, 2002. This is eleventh, in the order, symposium organized by the Institute of Computer Science of Polish Academy of Sciences and devoted to new trends in (broadly understood) ArtificialIntelligence. The meetings started back to 1992. With small initial audience, workshops in the series grew to an important meeting of Polish and foreign scientists working at the universities in Europe, Asia and the Northern America. Over years, the workshops transformed into regular symposia devoted to latest trends in such fields like Machine Learning, Knowledge Discovery, Natural Language Processing, Knowledge Based Systems and Reasoning, and Soft Computing (i.e. Fuzzy and Rough Sets, Bayesian Networks, Neural Networks and Evolutionary Algorithms). At present, about 50-60 papers are accepted each year. Besides, for several years now, the symposia are accompanied by a number of tutorials, given by the outstanding scientists in their domain. The main topics of this year symposium included: * decision trees and other classifier systems * neural network and biologiccally motivated systems * clustering methods * handling imprecision and uncertainty * deductive, distributed and agent-based systems We were pleased to see the continuation of the last year trend towards an increase in the number of co-operative contributions and in the number and diversity of practical applications of theoretical research.