Domain Driven Data Mining

Domain Driven Data Mining

Longbing Cao (University of Technology, Sydney, Australia) and Chengqi Zhang (University of Technology, Sydney, Australia)
Copyright: © 2008 |Pages: 28
DOI: 10.4018/978-1-59904-960-1.ch008
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Abstract

Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to Quantitative Intelligence focused data-driven philosophy. It either views data mining as an autonomous data-driven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learnt from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as Domain-Driven Data Mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business.

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Table of Contents
Preface
David Taniar
Chapter 1
Riadh Ben Messaoud, Sabine Loudcher Rabaséda, Rokia Missaoui, Omar Boussaid
Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting... Sample PDF
OLEMAR: An Online Environment for Mining Association Rules in Multidimensional Data
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Chapter 2
Yun Sing Koh, Richard O’Keefe, Nathan Rountree
Association rules are patterns that offer useful information on dependencies that exist between the sets of items. Current association rule mining... Sample PDF
Interestingness Measures for Association Rules: What Do They Really Measure?
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Chapter 3
Qin Ding, Gnanasekaran Sundarraj
With the growing usage of XML in the World Wide Web and elsewhere as a standard for the exchange of data and to represent semi-structured data... Sample PDF
Mining Association Rules from XML Data
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Chapter 4
Yue-Shi Lee, Show-Jane Yen
Web mining is one of the mining technologies, which applies data mining techniques in large amount of web data to improve the web services. Web... Sample PDF
A Lattice-Based Framework for Interactively and Incrementally Mining Web Traversal Patterns
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Chapter 5
Tushar, Tushar, Shibendu Shekhar Roy, Dilip Kumar Pratihar
Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters... Sample PDF
Determination of Optimal Clusters Using a Genetic Algorithm
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Chapter 6
ABM Shawkat Ali
Clustering technique in data mining has received a significant amount of attention from machine learning community in the last few years as one of... Sample PDF
K-means Clustering Adopting rbf-Kernel
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Chapter 7
Pradeep Kumar, P. Radha Krishna, Raju S. Bapi, T. M. Padmaja
In recent years, advanced information systems have enabled collection of increasingly large amounts of data that are sequential in nature. To... Sample PDF
Advances in Classification of Sequence Data
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Chapter 8
Justin Zhan
To conduct data mining, we often need to collect data from various parties. Privacy concerns may prevent the parties from directly sharing the data... Sample PDF
Using Cryptography For Privacy-Preserving Data Mining
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Chapter 9
Domain Driven Data Mining  (pages 196-223)
Longbing Cao, Chengqi Zhang
Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications.... Sample PDF
Domain Driven Data Mining
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Chapter 10
Model Free Data Mining  (pages 224-252)
Can Yang, Jun Meng, Shanan Zhu, Mingwei Dai
Input selection is a crucial step for nonlinear regression modeling problem, which contributes to build an interpretable model with less... Sample PDF
Model Free Data Mining
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Chapter 11
John Wang, Xiaohua Hu, Dan Zhu
This research explores the effectiveness of data mining in a commercial perspective. Statistical issues are specified first. Data accuracy and... Sample PDF
Minimizing the Minus Sides of Mining Data
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Chapter 12
Tu Bao Ho, Thanh Phuong Nguyen, Tuan Nam Tran
The objective of this paper is twofold. First is to provide a survey of computational methods for protein-protein interaction (PPI) study. Second is... Sample PDF
Study of Protein-Protein Interactions from Multiple Data Sources
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Chapter 13
Anthony Scime, Gregg R. Murray, Wan Huang, Carol Brownstein-Evans
Immense public resources are expended to collect large stores of social data, but often these data are under-examined thereby missing potential... Sample PDF
Data Mining in the Social Sciences and Iterative Attribute Elimination
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Chapter 14
Marco A. Alvarez, SeungJin Lim
Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The... Sample PDF
A Machine Learning Approach for One-Stop Learning
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About the Contributors