- Full Description
This book presents inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The book provides an overview of the state-of-the art in this novel research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the unification of pattern mining approaches through constraint programming, the clarification of the relationship between mining local patterns and global models, and the proposed integrative frameworks and approaches for inductive databases. On the application side, applications to practically relevant problems from bioinformatics are presented to attract additional attention from a wider audience.The primary audience consists of scientists and graduate students in computer science and bio-informatics. Potential readers are likely to attend conferences on databases, data mining/ machine learning, and bio-informatics.
- Table of Contents
Table of Contents
- Part 1 Introduction & Framework.
- Twelve Years After: A Historical Perspective on Inductive Databases.
- A Data Mining Framework and Ontology.
- Data Mining Query Languages, Mining Views, and Algebras.
- The Assessment of Data Mining Results through Randomization.
- Part 2 Constraint
- based Mining Techniques.
- Generalizing Item set Mining in a Constraint Programming Setting From Local Patterns to Classification Models.
- Constrained Induction of Predictive Clustering Trees.
- Constrained Clustering: An Overview.
- Probabilistic Inductive Querying Using ProbLog.
- Part 3 Inductive Databases: Integration Approaches.
- An Inductive Database based on Mining.
- SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model Experiment Databases.
- Inductive Scientific Databases for Equation Discovery.
- Part 4 Applications in Bioinformatics.
- Robot Scientists, Inductive Queries, and Drug Design.
- Predicting Gene Function using Predictive Clustering Trees.
- Analysis of Gene Expression Data with Predictive Clustering Trees Using a Solver Over String Pattern Domain to Analyse Gene Promoter Sequences.
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