Advanced Methods for Knowledge Discovery from Complex Data by Ujjwal MaulikAdvanced Methods for Knowledge Discovery from Complex Data by Ujjwal Maulik

Advanced Methods for Knowledge Discovery from Complex Data

byUjjwal MaulikEditorLawrence B. Holder, Diane J. Cook

Paperback | October 22, 2010

Pricing and Purchase Info


Earn 1,375 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


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 following chapters.
Title:Advanced Methods for Knowledge Discovery from Complex DataFormat:PaperbackDimensions:369 pages, 23.5 × 15.5 × 0.07 inPublished:October 22, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1849969914

ISBN - 13:9781849969918

Look for similar items by category:


Table of Contents

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.