Accuracy (138) | ![]() |
Application programming interface (143) | ![]() |
Artificial intelligence (117) | ![]() |
Artificial neural network (5) | ![]() |
Association rule (3) | ![]() |
Back propagation (1) | ![]() |
Cardinality (9) | ![]() |
Cart (2) | ![]() |
Cluster analysis (70) | ![]() |
Clustering (199) | ![]() |
Collinearity (6) | ![]() |
Conditional probability (3) | ![]() |
Coverage (1061) | ![]() |
Customer relationship management (22) | ![]() |
Data mining (37) | ![]() |
Decision trees (2) | ![]() |
Entropy (4) | ![]() |
Error rate (8) | ![]() |
Exploratory data analysis (1) | ![]() |
Factor analysis (112) | ![]() |
Front office (10) | ![]() |
Fuzzy logic (6) | ![]() |
Fuzzy set (4) | ![]() |
Fuzzy system (1) | ![]() |
Genetic algorithm (3) | ![]() |
Intelligent agent (6) | ![]() |
Knowledge discovery (9) | ![]() |
Nearest neighbor (1) | ![]() |
Occam s razor (1) | ![]() |
Overfitting (1) | ![]() |
Sensitivity analysis (101) | ![]() |
Structured query language (1) | ![]() |
Targeted-marketing (3) | ![]() |
Text mining (12) | ![]() |
Time-series forecasting (1) | ![]() |
Web mining (1) | ![]() |
Other techniques of knowledge discovery, such as neural networks, genetic algorithms, induction or other multistrategy machine learning hybrid tools PIA 91
PIA 91 PIATESKI G.,FRAWLEY W.,Knowledge discovery in Databases, MIT Press, Cambridge, MA, 1991. PLA 14 PLANETOSCOPE, available at http://www. planetoscope. com/Avion/109-nombre-de-vols-d-avions-dans-le-monde. html, 2014.
indicators, 176 knowledge discovery, 9 ecology, 54 economy 47,55, 70,73, 123 cultivators, 60,63, 64,137, 178 management, 46,67, 71,103, 139 processing, 177 knowledge-based systems, 99 Kohala Center, 128
Replaying history on process models for conformance checking and performance analysis. WIRES Data mining and Knowledge discovery, 2 (2), 182 192.
Data mining and Knowledge discovery, 14 (2), 245 304. Barros, A.,Decker, G.,Dumas, M, . & Weber, F. 2007).
or specialised termsKnowledge discovery'an ability and willingness to engage in a, by way of finding out about and learning from the culture (e g. customs, practices and values) of the customers, clients or business partners you will be working wth
Proc. of the Workshop on Ubiquitous Knowledge discovery for Users at ECML/PKDD, pp. 51 64 (2006) 15.
From a technical perspective, the most difficult piece in the realization of the whole use case was the knowledge discovery about the nonfunctional behaviour of the different components, e g. the performance characteristics of the middleware.
Data mining and Knowledge discovery 27 (2): 225 58. Available at http://link. springer. com/article/10.1007/s10618-012-0289-3?
< Back - Next >
Overtext Web Module V3.0 Alpha
Copyright Semantic-Knowledge, 1994-2011