Pattern recognition

Pattern recognition is the field in which algorithms learn to find stable structures in data: classes, clusters, repetitive forms, motifs, and dependencies. The goal is to automatically identify "sense patterns" and use them for predictions, similarity searches, segment detection, and decision making.

1) Setting tasks

Classification: assigning an object to a class (fraud/non-fraud, event type).
Multi-label/multi-label classification: multiple classes at the same time.
Clustering and segmentation: grouping without labels, highlighting anomalous/niche groups.
Ranking/similarity search: relevance ordering, nearest neighbors.
Segmentation of structures: markup of object parts (image, log record, session).
Sequence recognition: labels for time series/logs/text.
Extracting rules and motives: frequent sets/sequences, associative rules.
Graph tasks: node/edge classification, community discovery.

Training modes:

Supervisory (there are tags), non-supervisory (clustering/rules), semi-supervisory (pseudo tags), self-supervised (self-supervised: contrastive/augmentation).

2) Data and views

Tabular: numerical and categorical characteristics; interactions, window statistics.
Time series/event logs: lags, trends, seasonality, DTW characteristics, spectral characteristics.
Text: tokens/embeddings (Bag-of-Words, TF-IDF, word2vec/fastText, BERT-embeddings), n-grams, key phrases.
Images/Audio: Spectra/Chalk Features, Local Descriptors (SIFT/HOG), CNN Global Embeddings.
Graphs: adjacency matrix, node2vec/DeepWalk, GNN-embeddings.
Multi-modality: late/early fusion, cross-attention.

Key principles: point-in-time correctness, absence of future leaks, standardization/robast scaling, category coding (one-hot/target/hash), accurate handling of omissions and emissions.

3) Methods

3. 1 Classical statistical and metric

Linear models: logistic/linear regression with regularization (L1/L2/Elastic Net).
Nearest neighbor methods: kNN, ball-tree/FAISS for embedding searches.
SVM/kernel methods: RBF/polynomial kernels, one-class SVM (for "norm").
Naive Bayes/hybrids: quick baselines for text/categories.
Dimensionality reduction: PCA/ICA/t-SNE/UMAP for visualization and preprocessing.

3. 2 Trees and ensembles

Random Forest, Gradient Boosting (XGBoost/LightGBM/CatBoost): strong baselines on the plate, resistant to mixed types of features, give the importance of signs.
Stacking/blending: ensembles from heterogeneous models.

3. 3 Neural networks by modalities

Sequences: RNN/LSTM/GRU, Temporal Convolutional Networks, Transformers (including for long rows).
Computer vision: CNN/ResNet/ConvNeXt, Vision Transformer; detection/segmentation (Faster/Mask R-CNN, U-Net).
Text: Encoder-only (BERT class), Encoder-Decoder (T5), classification/ranking/NER.
Graphs: GCN/GAT/GraphSAGE for structural patterns.

3. 4 Pattern Mining and Rules

Frequent sets/sequences: Apriori/Eclat, FP-Growth, PrefixSpan.

Associative rules: support/lift/confidence; Filtering by business value

Time series motifs/patterns: Matrix Profile, SAX, segmentation by mode changes.

4) Validation and experiments

Splits: i.i.d. K-fold for stationary data; temporal CV/rolling-windows for sequences.
Stratification and grouping: control of leaks between users/sessions/campaigns.
Out-of-time test: final check on the "future" period.
Baselines: naive rules, frequency predictions, simple logreg/GBM.

5) Quality metrics

Classification: accuracy (on balance), ROC-AUC, PR-AUC on rare classes, logloss, F1, precision/recall @ k, NDCG/Lift for ranking.

Clustering: silhouette, Davies-Bouldin, Calinski-Harabasz; external - ARI/NMI in the presence of the "gold standard."

Image segmentation: IoU/Dice.
Sequences/NER: token-/entity-level F1; time-to-first-correct for online recognition.
Business metrics: incremental profit, reduced manual load, processing speed.

6) Interpretability and trust

Global: importance of feature (gain/permutation), PDP/ICE, SHAP-summary.
Locally: SHAP/LIME/Anchors to explain a specific solution.
For rules: transparent metrics (support/lift), rule conflicts, coverage.

Embedding visualization: UMAP/t-SNE for pattern and cluster "maps."

7) Data robustness and quality

Robustness: resistant scalers (median/MAD), vinzorization, protection against emissions.
Drift: distribution monitoring (PSI/JS/KL), target drift and feature, periodic recalibration.
Fairness: comparison of errors by segment, restrictions on FPR/TPR, bias-skill.
Privacy/compliance: minimization of fields, pseudonymization, access by roles.

8) Pipeline (from data to production)

1. Define task and KPIs (and "gold" validation scenarios).

2. Data Collection/Preparation - Schemas, Deduplication, Time Zones, Aggregates, and Embeddings

3. Baselines: simple rules/logreg/GBM; sanity-checks.
4. Enrichment of representations: domain characteristics, embeddings of modalities, feature store.
5. Training and selection: grids/bayes optimization, early stop, cross validation.
6. Calibration and thresholds: Platt/isotonic, selection of thresholds for business value.
7. Deploy: REST/gRPC batch/online; versioning artifacts and schematics.
8. Monitoring: quality (ML-metrics + business), distribution, delays; alerts and runibooks.
9. Retraining: schedule/by drift event; A/B/canary releases.

9) Practical patterns by scenario

Fraud and risk scoring (plate): GBM/stacking → add graph characteristics (connections by devices/cards) and GNN; strict latency restrictions; optimization by PR- AUC/recall@FPR≤x%.
Personalization and content (ranking): trainable user/object embeddings + binary click signal; loss: pairwise/listwise; online updates.
Log/sequence analytics: TCN/Transformer, contrastive self-supervised on augmentation; detection of motives and mode changes.
Text recognition of intentions/themes: BERT class, fine-tuning; interpretability through/attention key tokens.
Images/Video (Quality Control/Incidents): Defect Classification, Localization (Grad-CAM/Mask R-CNN), IoU Metrics and Escalation Rules.
Graphs (communities/fraudulent chains): GNN + graph anomaly heuristics (degree/triangles/class coefficient).

10) Model Selection: Simple Decision Matrix

Data	Purpose	Recommended start
Tabular, mixed types	Classification/ranking	LightGBM/CatBoost + SHAP interpretability
Time sequences	Time stamps	TCN/Transformer; for simple ones - logreg on lag fiches
Text	Topics/Intentions	BERT class + tokenization; baseline - TF-IDF + Logreg
Images	Classification/defects	ResNet/ConvNeXt; baseline - MobileNet
Columns	Sites/Communities	GCN/GAT; baseline - node2vec + logreg
Untagged	Segmentation/search for motives	K-means/HDBSCAN, Matrix Profile, associative rules

11) Error and Overfit Mitigation Techniques

Regularization (L1/L2/dropout), early stop, data augmentation and mixup/cutout (for CV/audio).
Leak control: strict time splits, group cuts, "freezing" of embeddings for validation.
Probability calibration and stable thresholds under business constraints.
Ensembling/Model soup for shear resistance.

12) Pre-release checklist

Correct splits (temporal/group), no leaks
Stable metrics on OOT window and key segments
Probabilities are calibrated; thresholds/cost matrix defined
SLOs initiated: quality, latency, availability
Inference logs, artifact versions, data contracts
Retraining plan and degradation strategy (fallback)
Documentation and Runibooks (RCA, Errors, Escalation Paths)

Mini Glossary

Pattern mining: finding frequently occurring sets/sequences.
Embedding: A vector representation of an object that preserves semantics/similarity.

Contrastive learning: learning that brings together "similar" examples and divides "different."

Silhouette/NMI/ARI: clustering quality metrics.
IoU/Dice: segmentation quality metrics.

Total

Pattern recognition is not only the choice of "model X," but the discipline of representations, correct validation, and the operational cycle. Strong performances (feature/embeddings), stable baselines (GBM/SVM/simple CNN), high-quality splits and strict monitoring in the prods give the greatest return. Add complexity (deep architectures, multi-modalities, graphs) only when it brings a measurable increase in ML and business metrics.

Pattern recognition