Mining Complex Data
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This is the first book focusing specifically on mining complex data. The papers collected in it were selected from workshop papers presented annually since 2006 and address issues dealing with each step of the mining data process.

Produktbeschreibung
This is the first book focusing specifically on mining complex data. The papers collected in it were selected from workshop papers presented annually since 2006 and address issues dealing with each step of the mining data process.
  • Produktdetails
  • Studies in Computational Intelligence 165
  • Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
  • Softcover reprint of hardcover 1st ed. 2009
  • Seitenzahl: 316
  • Erscheinungstermin: 28. Oktober 2010
  • Englisch
  • Abmessung: 235mm x 155mm x 17mm
  • Gewicht: 474g
  • ISBN-13: 9783642099809
  • ISBN-10: 3642099807
  • Artikelnr.: 32388108
Inhaltsangabe
General Aspects of Complex Data.- Using Layout Data for the Analysis of Scientific Literature.- Extracting a Fuzzy System by Using Genetic Algorithms for Imbalanced Datasets Classification: Application on Down's Syndrome Detection.- A Hybrid Approach of Boosting Against Noisy Data.- Dealing with Missing Values in a Probabilistic Decision Tree during Classification.- Kernel-Based Algorithms and Visualization for Interval Data Mining.- Rules Extraction.- Evaluating Learning Algorithms Composed by a Constructive Meta-learning Scheme for a Rule Evaluation Support Method.- Mining Statistical Association Rules to Select the Most Relevant Medical Image Features.- From Sequence Mining to Multidimensional Sequence Mining.- Tree-Based Algorithms for Action Rules Discovery.- Graph Data Mining.- Indexing Structure for Graph-Structured Data.- Full Perfect Extension Pruning for Frequent Subgraph Mining.- Parallel Algorithm for Enumerating Maximal Cliques in Complex Network.- Community Finding of Scale-Free Network: Algorithm and Evaluation Criterion.- The k-Dense Method to Extract Communities from Complex Networks.- Data Clustering.- Efficient Clustering for Orders.- Exploring Validity Indices for Clustering Textual Data.