| 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) |
and van der Aalst (2013) for example present a technique to support process participants in making risk-informed decisions by traversing decision trees generated from the logs of past process executions.
Popular techniques related to storing and enforcing high-level information include neural networks, expert systems, statistical association, conditional probability distributions, different kinds of monotonic and nonmonotonic, fuzzy logic, decision trees, static and dynamic
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