Mining of Data with Complex Structures:
- Clarifies the type and nature of data with complex structure
including sequences, trees and graphs
- Provides a detailed background of the state-of-the-art of sequence
mining, tree mining and graph mining.
- Defines the essential aspects of the tree mining problem: subtree
types, support definitions, constraints.
- Outlines the implementation issues one needs to consider when
developing tree mining algorithms (enumeration strategies, data
structures, etc.)
- Details the Tree Model Guided (TMG) approach for tree mining and
provides the mathematical model for the worst case estimate of
complexity of mining ordered induced and embedded subtrees.
- Explains the mechanism of the TMG framework for mining
ordered/unordered induced/embedded and distance-constrained embedded
subtrees.
- Provides a detailed comparison of the different tree mining
approaches highlighting the characteristics and benefits of each
approach.
- Overviews the implications and potential applications of tree mining
in general knowledge management related tasks, and uses Web, health and
bioinformatics related applications as case studies.
- Details the extension of the TMG framework for sequence mining
- Provides an overview of the future research direction with respect to
technical extensions and application areas
The primary audience is 3rd year, 4th year undergraduate students,
Masters and PhD students and academics. The book can be used for both
teaching and research. The secondary audiences are practitioners in
industry, business, commerce, government and consortiums, alliances and
partnerships to learn how to introduce and efficiently make use of the
techniques for mining of data with complex structures into their
applications. The scope of the book is both theoretical and practical
and as such it will reach a broad market both within academia and
industry. In addition, its subject matter is a rapidly emerging field
that is critical for efficient analysis of knowledge stored in various
domains.
