Search results for “выявление аномалий”

Anomaly and novelty detection; heuristic anomaly scores: local density, isolation forest, a mixture with a background component, robust principal component analysis; outlier removal in nonparametric regression, the LOWESS algorithm; anomaly as the value of the loss function; the theory of robust learning: slowly growing functions, iterative reweighting of objects with IRS and IRLS, robust loss functions, the median, the quantile and the censored mean, robust aggregation functions and their smoothing, the IR-ERM algorithm; one-class SVM, learning from positive and unlabeled objects (PU-learning), problems with new and unknown classes.

The clustering problem and its ill-posedness, the aims of clustering, types of cluster structure, sensitivity to the choice of metric; the problem of semi-supervised learning and transductive learning, why it reduces neither to classification nor to clustering; criteria of clustering quality, the silhouette coefficient, the BCubed measures of precision and recall; the k-means method (Lloyd's algorithm) as a simplification of the EM algorithm for a mixture of Gaussians; the DBSCAN algorithm; agglomerative hierarchical clustering, the Lance–Williams formula and its special cases, the dendrogram, monotonicity, contracting and dilating distances, Ward's distance; semi-supervised learning based on classification: self-training, co-training, co-learning, the transductive SVM, expectation regularisation (XR).

The interpretability of models; decision trees and the greedy ID3 algorithm; the axioms of impurity, the entropy and Gini criteria; handling missing values; the merits and drawbacks of the greedy strategy; tree pruning (post-pruning); CART regression trees and Minimal Cost-Complexity Pruning; rule induction: logical regularities, families of rules (conjunctions, syndromes, half-planes, balls), search metaheuristics, a tree as a covering set of conjunctions; the Pareto front in (p,n) space, logical and statistical regularities, Fisher's exact test, informativeness criteria; building a classifier from regularities.

Association rule mining as unsupervised learning: binary features, support and frequent itemsets, the association rule, minimum support MinSupp and minimum confidence MinConf; market basket analysis; the link between association rules and logical regularities; the anti-monotonicity of support; the APriori algorithm: breadth-first search for frequent itemsets and recursive extraction of rules; modifications: search on a random subsample, item hierarchies, incremental and decremental algorithms; the FP-growth algorithm: the prefix FP-tree, building it in one pass over the transaction database, computing the support of any itemset from the tree, the conditional FP-tree and recursive search for frequent itemsets; the speed of FP-growth compared with APriori.