Knowledge Discovery in Inductive Databases: Third International Workshop, KDID 2004, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers / Edition 1
by Arno Siebes
This book constitutes the thoroughly refereed joint postproceedings of the Third International Workshop on Knowledge Discovery in Inductive Databases, KDID 2004, held in Pisa, Italy in September 2004 in association with ECML/PKDD.
Inductive Databases support data mining and the knowledge discovery process in a natural way. In addition to usual data, an inductive
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This book constitutes the thoroughly refereed joint postproceedings of the Third International Workshop on Knowledge Discovery in Inductive Databases, KDID 2004, held in Pisa, Italy in September 2004 in association with ECML/PKDD.
Inductive Databases support data mining and the knowledge discovery process in a natural way. In addition to usual data, an inductive database also contains inductive generalizations, like patterns and models extracted from the data. This book presents nine revised full papers selected from 23 submissions during two rounds of reviewing and improvement together with one invited paper. Various current topics in knowledge discovery and data mining in the framework of inductive databases are addressed.
Product Details
- ISBN-13:
- 9783540250821
- Publisher:
- Springer Berlin Heidelberg
- Publication date:
- 04/06/2005
- Series:
- Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI Series, #3377
- Edition description:
- 2005
- Pages:
- 200
- Product dimensions:
- 0.44(w) x 6.14(h) x 9.21(d)
Table of Contents
Invited Paper.- Models and Indices for Integrating Unstructured Data with a Relational Database.- Contributed Papers.- Constraint Relaxations for Discovering Unknown Sequential Patterns.- Mining Formal Concepts with a Bounded Number of Exceptions from Transactional Data.- Theoretical Bounds on the Size of Condensed Representations.- Mining Interesting XML-Enabled Association Rules with Templates.- Database Transposition for Constrained (Closed) Pattern Mining.- An Efficient Algorithm for Mining String Databases Under Constraints.- An Automata Approach to Pattern Collections.- Implicit Enumeration of Patterns.- Condensed Representation of EPs and Patterns Quantified by Frequency-Based Measures.
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