THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING
International Journal of Pattern Recognition and Artificial IntelligencePublished 1 June 2007
Longbing Cao, Chengqi Zhang
Citations70
SJR quartileQ3
SJR score0.27
SNIP0.40
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Abstract
Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, real-world applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligence is highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data.
Keywords
Computer Science
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