Mining Complex Data

Häftad, Engelska, 2010

Av Djamel A. Zighed, Shusaku Tsumoto, Zbigniew W. Ras, Hakim Hacid

2 759 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g.

Produktinformation

Utgivningsdatum2010-10-28
Mått155 x 235 x 18 mm
Vikt482 g
FormatHäftad
SpråkEngelska
SerieStudies in Computational Intelligence
Antal sidor302
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783642099809

Tillhör följande kategorier

Teknik: allmänt inom Naturvetenskap och teknik
Artificiell intelligens inom Data och it
Databaser inom Data och it

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.