AHP Construct Mining Component (ACMC), which is a new term, is an enhancement of applicable structure for multidimensional and multi-level complex dataflow. ACMC is applied into data mining framework and different processing components with the purpose are improvement on numerous aspects in multiply level. ACMC provides not only an integrated platform to support different processing components with comprehensive and systemic methodology but also provides controllable strategy for whole processing. The instances of KPI (key performance indicator) and CSF (critical success factor) are the key points and foundation of the whole data mining structure. Mode-Refresh and Model-Evaluation are recognized as engines of the data mining machine. Influencing factor that come from these engines will influence decision constrictions. ACMC supports combination of different mining component from strategy level, tactical level to abstractive level, and then provide the successful model component for the whole data mining processing. ACMC is a new direction of the decision of KDD (Knowledge Discovery in Database).
[1]
Thomas L. Saaty,et al.
Group Decision Making: Drawing Out and Reconciling Differences
,
2007
.
[2]
Geoff Hulten,et al.
Mining time-changing data streams
,
2001,
KDD '01.
[3]
Gregory Piatetsky-Shapiro,et al.
Knowledge Discovery in Databases: An Overview
,
1992,
AI Mag..
[4]
Jitender S. Deogun,et al.
Sequential Association Rule Mining with Time Lags
,
2004,
Journal of Intelligent Information Systems.
[5]
Weihua Li,et al.
The Study of Multidimensional-Data Flow of Fishbone Applied for Data Mining
,
2009,
2009 Seventh ACIS International Conference on Software Engineering Research, Management and Applications.
[6]
Geoff Hulten,et al.
Mining high-speed data streams
,
2000,
KDD '00.