
Advanced Methods for Knowledge Discovery from Complex Data
Springer (Publisher)
Published on 22. October 2010
Book
Paperback/Softback
XVIII, 369 pages
978-1-84996-991-8 (ISBN)
Description
The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the followingchapters.
More details
Series
Edition
Softcover reprint of hardcover 1st ed. 2005
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Research
Illustrations
XVIII, 369 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 21 mm
Weight
587 gr
ISBN-13
978-1-84996-991-8 (9781849969918)
DOI
10.1007/1-84628-284-5
Schweitzer Classification
Other editions
Additional editions

Ujjwal Maulik | Lawrence B. Holder | Diane J. Cook
Advanced Methods for Knowledge Discovery from Complex Data
Book
11/2005
Springer
€160.49
Shipment within 15-20 days
Content
Foundations.- Knowledge Discovery and Data Mining.- Automatic Discovery of Class Hierarchies via Output Space Decomposition.- Graph-based Mining of Complex Data.- Predictive Graph Mining with Kernel Methods.- TreeMiner: An Efficient Algorithm for Mining Embedded Ordered Frequent Trees.- Sequence Data Mining.- Link-based Classification.- Applications.- Knowledge Discovery from Evolutionary Trees.- Ontology-Assisted Mining of RDF Documents.- Image Retrieval using Visual Features and Relevance Feedback.- Significant Feature Selection Using Computational Intelligent Techniques for Intrusion Detection.- On-board Mining of Data Streams in Sensor Networks.- Discovering an Evolutionary Classifier over a High-speed Nonstatic Stream.