Domain driven data mining
Author(s)
Bibliographic Information
Domain driven data mining
Springer, c2010
Available at 4 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Formerly CIP Uk
Includes bibliographical references and index
Description and Table of Contents
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.
Table of Contents
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.
by "Nielsen BookData"