
Domain Driven Data Mining
Springer (Publisher)
Published on 3. December 2014
Book
Paperback/Softback
XVI, 248 pages
978-1-4899-8507-1 (ISBN)
Description
Data mining has emerged as one of the most active areas in information and c- munication technologies(ICT). With the boomingof the global economy,and ub- uitouscomputingandnetworkingacrosseverysectorand business,data andits deep analysis becomes a particularly important issue for enhancing the soft power of an organization, its production systems, decision-making and performance. The last ten years have seen ever-increasingapplications of data mining in business, gove- ment, social networks and the like. However, a crucial problem that prevents data mining from playing a strategic decision-support role in ICT is its usually limited decision-support power in the real world. Typical concerns include its actionability, workability, transferability, and the trustworthy, dependable, repeatable, operable and explainable capabilities of data mining algorithms, tools and outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data mining, as well as the ?ndings, thoughts and lessons learned in conducting several large-scale real-world data mining bu- ness applications.
The aim and objective of domain driven data mining is to study effective and ef?cient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-taking.
The aim and objective of domain driven data mining is to study effective and ef?cient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-taking.
Reviews / Votes
From the reviews:
"This book offers a comprehensive discussion of domain-driven data mining (D3M), a set of techniques and methodologies that aim to discover actionable knowledge that can be presented to business decision makers in order to enable them to make informed decisions. . The resulting approach is an exploration of possibilities for enhancing the decision-support power of data mining and knowledge discovery. . This well-written and practical book summarizes domain-specific problem-solving methods for the delivery of actionable knowledge, and is suitable for researchers and students . ." (Alessandro Berni, ACM Computing Reviews, November, 2010)More details
Edition
2010 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XVI, 248 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 15 mm
Weight
406 gr
ISBN-13
978-1-4899-8507-1 (9781489985071)
DOI
10.1007/978-1-4419-5737-5
Schweitzer Classification
Other editions
Additional editions

Longbing Cao | Philip S. Yu | Chengqi Zhang
Domain Driven Data Mining
Book
01/2010
Springer
€106.99
Shipment within 15-20 days

Longbing Cao | Philip S. Yu | Chengqi Zhang
Domain Driven Data Mining
E-Book
01/2010
1st Edition
Springer
€96.29
Available for download
Persons
Prof Longbing Cao is the Distinguished Chair in Artificial Intelligence (AI) and an Australian Research Council Future Fellow (professor) at the School of Computing, Macquarie University, Australia. Before joining Macquarie, he was the Founding Director of the Advanced Analytics Institute at the University of Technology Sydney and was a chief technology officer. Prof Cao is an internationally active research leader in AI, data science, and machine learning in both research and practice. He chaired many ACM and IEEE chapters and task forces and served as general chairs, program chairs, and steering committee chairs of prestigious international conferences. He has served as editors-in-chief and on editorial boards of core AI and data science journals. His significant research leadership and socioeconomic benefits have been recognized by an individual Australia's Eureka Prize and elected as an ACM Distinguished Scientist by ACM.
Content
Challenges and Trends.- Methodology.- Ubiquitous Intelligence.- Knowledge Actionability.- AKD Frameworks.- Combined Mining.- Agent-Driven Data Mining.- Post Mining.- Mining Actionable Knowledge on Capital Market Data.- Mining Actionable Knowledge on Social Security Data.- Open Issues and Prospects.- Reading Materials.