Data Mining Algorithms: Explained Using R

Index

1. 0-1 loss, see loss function, 0-1
2. .632 bootstrap
3. absolute loss, see loss function, absolute
4. accuracy
5. AdaBoost
   1. instance weighting for
   2. model weighting for
   3. multiclass
6. AdaBoost.MH
7. adaptive boosting, see AdaBoost
8. add-one smoothing, see probability, Laplace estimate of
9. agglomerative clustering
10. aggregation
11. AHC, see hierarchical clustering, agglomerative
12. algorithm randomization
13. anomaly detection
    1. by clustering
14. ANOVA, see F-test
15. AODE, see averaged one-dependence estimators
16. apparent disutility
17. apparent utility
18. attribute
    1. continuous
       1. discretization of, see discretization
    2. discrete
       1. aggregation of, see aggregation
       2. binary encoding of, see attribute encoding
    3. hidden
    4. input, see input attribute
    5. nominal
    6. numeric, see attribute, continuous
    7. observable
    8. ordinal
    9. target, see target attribute
19. attribute encoding
20. attribute sampling
21. attribute selection
    1. motivation for
    2. target algorithm for
    3. target task for
22. attribute selection filter
    1. consistency-based, see consistency-based filter
    2. correlation-based, see correlation-based filter
    3. random forest see random forest, for attribute selection
    4. simple statistical, see simple statistical filter
23. attribute selection search
    1. backward, see backward elimination
    2. filter-driven
    3. forward, see forward selection
    4. greedy
    5. initial state for
    6. operator for
    7. preference criteria for
    8. stop criteria for
24. attribute selection wrapper
    1. seach strategy for
    2. subset evaluation for
25. attribute transformation
    1. as modeling, see attribute transformation, modeling
    2. for ensemble modeling ...