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Springer Mining Data with Complex Structures - Springer Study 333

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Description

Mining of Data with Complex Structures explores advanced computational methods for analysing datasets with complex structures, including sequences, trees and graphs. This comprehensive volume from Springer's Studies in Computational Intelligence series provides detailed background on the state-of-the-art in sequence mining, tree mining and graph mining techniques. The book clarifies the types and nature of data with complex structures, defines essential aspects of the tree mining problem including subtree types and support definitions, and outlines critical implementation considerations for developing tree mining algorithms such as enumeration strategies and data structures. It also details the Tree Model Guided approach for tree mining and provides the mathematical foundations necessary for understanding these advanced data analysis methods. Ideal for researchers, practitioners and students working in AI, machine learning and computational intelligence.

Key Features

Springer publication covering sequences, trees and graphs as complex data structures with comprehensive background on current mining methodologies.

Defines essential tree mining problem aspects including subtree types, support definitions and constraints for structured data analysis.

Outlines practical implementation issues for tree mining algorithms such as enumeration strategies and data structure selection.

Details the Tree Model Guided approach for tree mining with mathematical foundations for algorithm development and application.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
25 February 2013
Listed Since
08 February 2013

Barcode

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