Data Mining

Theory, Methodology, Techniques, and Applications

AvGraham J. Williams,Simeon J. Simoff

Häftad, Engelska, 2006

Del i serien Lecture Notes in Computer Science

577 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

This volume provides a snapshot of the current state of the art in data mining, presenting it both in terms of technical developments and industrial applications. The collection of chapters is based on works presented at the Australasian Data Mining conferences and industrial forums. Authors include some of Australia's leading researchers and practitioners in data mining. The volume also contains chapters by regional and international authors.

Produktinformation

Utgivningsdatum:2006-02-20
Mått:155 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:331
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540325475

Utforska kategorier

Databaser inom Data och IT

Innehållsförteckning

1: State-of-the-Art in Research.- Generality Is Predictive of Prediction Accuracy.- Visualisation and Exploration of Scientific Data Using Graphs.- A Case-Based Data Mining Platform.- Consolidated Trees: An Analysis of Structural Convergence.- K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results.- Efficiently Identifying Exploratory Rules’ Significance.- Mining Value-Based Item Packages – An Integer Programming Approach.- Decision Theoretic Fusion Framework for Actionability Using Data Mining on an Embedded System.- Use of Data Mining in System Development Life Cycle.- Mining MOUCLAS Patterns and Jumping MOUCLAS Patterns to Construct Classifiers.- A Probabilistic Geocoding System Utilising a Parcel Based Address File.- Decision Models for Record Linkage.- Intelligent Document Filter for the Internet.- Informing the Curious Negotiator: Automatic News Extraction from the Internet.- Text Mining for Insurance Claim Cost Prediction.- An Application of Time-Changing Feature Selection.- A Data Mining Approach to Analyze the Effect of Cognitive Style and Subjective Emotion on the Accuracy of Time-Series Forecasting.- A Multi-level Framework for the Analysis of Sequential Data.- 2: State-of-the-Art in Applications.- Hierarchical Hidden Markov Models: An Application to Health Insurance Data.- Identifying Risk Groups Associated with Colorectal Cancer.- Mining Quantitative Association Rules in Protein Sequences.- Mining X-Ray Images of SARS Patients.- The Scamseek Project – Text Mining for Financial Scams on the Internet.- A Data Mining Approach for Branch and ATM Site Evaluation.- The Effectiveness of Positive Data Sharing in Controlling the Growth of Indebtedness in Hong Kong Credit Card Industry.