Data mining: foundations and intelligent paradigms v. 1 Clustering, association and classification

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field. Latest research in data mining using intelligent paradigms and their applications. State-of-the-Art title. Written by leading experts in this field. INDICE: Introductory Chapter. Clustering Analysis in Large Graphs with Rich Attributes. Temporal Data Mining: Similarity-Profiled Association Pattern. Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification. Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets. Randomized Algorithm of Finding the TrueNumber of Clusters Based on Chebychev Polynomial Approximation. Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters. DepMiner: A methodand a system for the extraction of significant dependencies. Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries. Text Clustering with Named Entities: A Model, Experimentation and Realization. Regional Association Rule Mining and Scoping from Spatial Data. Learning from Imbalanced Data:Evaluation Matters.

• ISBN: 978-3-642-23165-0
• Editorial: Springer Berlin Heidelberg
• Encuadernacion: Cartoné
• Páginas: 479
• Fecha Publicación: 23/09/2011
• Nº Volúmenes: 1
• Idioma: Inglés