Computational Intelligence in Data MiningG. Della Riccia, Giacomo Della Riccia, Rudolf Kruse, Hans-J. Lenz The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases” the book starts with a unified view on 'Data Mining and Statistics – A System Point of View'. Two special techniques follow: 'Subgroup Mining', and 'Data Mining with Possibilistic Graphical Models'. "Data Fusion and Possibilistic or Fuzzy Data Analysis” is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on learning of fuzzy models is studied. In the domain of "Classification and Decomposition” adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section "Learning and Data Fusion” learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models. |
Contents
DATA MINING AND STATISTICS A SYSTEMS POINT OF VIEW | 1 |
SUBGROUP MINING | 39 |
POSSIBILISTIC GRAPHICAL MODELS | 51 |
AN OVERVIEW OF POSSIBILISTIC LOGIC AND ITS APPLICATION TO NONMONOTONIC REASONING AND DATA FUSION | 69 |
ON THE SOLUTION OF FUZZY EQUATION SYSTEMS | 95 |
LEARNING FUZZY MODELS AND POTENTIAL OUTLIERS | 111 |
AN ALGORITHM FOR ADAPTIVE CLUSTERING AND VISUALISATION OF HIGHDIMENSIONAL DATA SETS | 127 |
LEARNING IN COMPUTER SOCCER | 141 |
CONTROLLING BASED ON STOCHASTIC MODELS | 153 |
Other editions - View all
Computational Intelligence in Data Mining Giacomo Della Riccia,Rudolf Kruse,Hans-J. Lenz Limited preview - 2014 |
Computational Intelligence in Data Mining Giacomo Della Riccia,Rudolf Kruse,Hans J. Lenz No preview available - 2014 |
Common terms and phrases
approach Artificial Intelligence association rules attributes Bayesian networks beam search Benferhat c-means classification trees clique tree cluster centers compute conditional independence graph Conf consists constraint context Data Mining data points data set database default rules defined denote describe description language deviation domain Dubois equation evaluation example formula fuzzy sets given granulated IEEE imprecise inconsistency induced inference input interpretation Jersey cattle KESO knowledge base Knowledge Discovery Kruse learning measure membership functions mining algorithms moral graph neighbour node nonmonotonic operator outliers patterns player possibilistic possibilistic logic possibilistic networks possibility distribution possibility theory Prade probabilistic probability distribution problem Proc projection Projection Pursuit Proposition quality function Reasoning relations RoboCup search space search strategy semantic simple Soccer specific Springer Statistics subgroup mining subset target variables techniques true tuples two-way tables Uncertainty University of Udine vector