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.

Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

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.

The comprehensive introduction to the art and science of locating facilities to make your organization more efficient, effective, and profitable. For the professional siting facilities, the task of translating organizational goals and objectives into concrete facilities requires a working familiarity with the theoretical and practical fundamentals of facility location planning and modeling. The first hands-on guide to using and developing facility location models, Network and Discrete Location offers a practiceoriented introduction to model-building methods and solution algorithms, complete with software to solve classical problems of realistic size and end-of-chapter exercises to enhance the reader's understanding. The text introduces the reader to the key classical location problems (covering, center, median, and fixed charge) which form the nucleus of facility location modeling. It also discusses real-life extensions of the basic models used in locating: production and distribution facilities, interacting services and facilities, and undesirable facilities. The book outlines a host of methodological tools for solving location models and provides insights into when each approach is useful and what information it provides. Designed to give readers a working familiarity with the basic facility location model types as well as an intuitive knowledge of the uses and limits of modeling techniques, Network and Discrete Location brings students and professionals alike swiftly from basic theory to technical fluency.

Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.