Intelligent Data Analysis

Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Pages Number: 334 Publisher: Science Pub. Date :2007-09-01 first edition this book to the main line data analysis. to the use of fuzzy sets. rough sets. genetic algorithms and artificial intelligence. machine learning methods such as analysis of data uncertainty inter-dependencies. the probability of causation. data classification and clustering. and for decision-making. strategies and integration analysis. The main features of the book: describes related fields in the most basic. the most significant results. we also introduce the latest developments in these areas. and the author's work in this area; for most theoretical proof is given. intuitive arguments and examples. the realization of the problem given a specific algorithm. This book can be used as knowledge discovery. intelligent information processing. decision analysis and other fields of research and development personnel. reference books. but also as a computer information systems graduate teaching materials. Contents: Chapter uncertainty probability theory and methods 1.1 1.2 Entropy-based concept of information entropy 1.2.1 1.2.2 1.2.3 Joint entropy and conditional entropy 1.3 discrete mutual information fuzzy set membership function 1.3.2 1.3.1 1.3.3 Fuzzy Sets and common set of fuzzy sets fuzzy relations 1.3.5 1.3.4 1.3.6 fuzzy sets of fuzzy numbers from 1 .3.7 1.4 Fuzzy rough set attribute reduction 1.4.1 1.4.2 1.4.3 rough rough set concepts probability rough sets fuzzy sets 1.4.4 1.4.5 rough approximation based on similar relations 1.5 GA 1.5.1 GA 1.5.2 GA bio-genetic basis of the basic concepts of the basic flow 1.5.3 GA 1.5.4 GA 1.5.5 Applications of model theory and genetic algorithm theory 1.5.6 Genetic algorithm convergence characteristics and applications References Notes References Chapter 2.1 Data dependency Data dependent functional dependency 2 2.1.1 2.1.3 .1.2 multi-valued dependence of non-loop connection dependency 2.1.4 2.2 data connection depends dependent relationship between the implied model is equivalent to 2.2.2 2.2.1 to connect the test according to Lai Yunhan 2.2.3 Functions 2.2.4 chase by Lai Yunhan test the relationship between the tables of fuzzy data dependencies 2.3 2.3.1 2.3.2 fuzzy fuzzy relational data model. the value of close to 2.3 degrees. 3 Fuzzy Relations 2.3.4 Fuzzy functional dependencies and operating multi-valued fuzzy connections rely rely 2.3.5 2.3.6 2.3.7 Laiyun Han fuzzy data ambiguity by fuzzy functional dependency constraints 2.3.8 Application Notes References References Chapter 3.1 Classification and cluster analysis classified the basic concepts of classification 3.1.1 Analysis 3.1.2 Introduction 3.1.3 Classification models 3.1.4 Classification Based on Decision Tree distance-based Bayesian classification 3.1.5 Classification 3.2 Overview 3.1.6 Other methods of cluster analysis classification clustering of the basic concepts 3.2.2 3.2.1 Data types and similarity measure 3 .2.3 3.2.4 Partition-based clustering hierarchical clustering based on density-based clustering 3.2.5 3.2.6 3.2.7 Fuzzy clustering methods other Notes References References section chapters 4.1 Bayesian network and Bayesian network of Markov network model in Figure 4.1.1 the relationship between the dependent Markov networks 4.1.3 4.1.2 4.2 Bayesian network 4.2.1 Bayesian network parameter learning structure 4.2.2 Bayesian network structure learning score - 4.2.3-based search methods rely on analysis of Markov network construction algorithm 4.2.4 Bayesian network constructed by the data dependencies network inference 4.3 Bayesian inference 4.3.1 Overview 4.3.2 Cutset conditioning reasoning 4.3.3 Clustering 4.4 Bayesian network inference methods together 4.4.1 Chain Figure 4.4.2 Bayesian network model aggregation Notes References References Chapter influence graph model based on decision analysis 5.1 The basic concept of statistical decision m. …