This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.

This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.

Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.

Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. MATLAB®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at the 10th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2015), held in the beautiful and historic city of Burgos (Spain), in June 2015. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate and analyze very complex issues and phenomena. This Conference is mainly focused on its industrial and environmental applications. After a through peer-review process, the SOCO 2015 International Program Committee selected 41 papers, written by authors from 15 different countries. These papers are published in present conference proceedings, achieving an acceptance rate of 40%. The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the International Program Committees for their hard work during the review process. This is a crucial issue for creation of a high standard conference and the SOCO conference would not exist without their help.