Gauge Usage Model
Discovering Structure from Multi-Modal Data Sources
Analyst time is valuable – spending it analyzing individual events / objects can be wasteful. With Gauge's hierarchical and interactive view of a whole database, an analyst can work at the right granularity. Gauge creates a hierarchy through (i) domain expert-driven feature engineering, (ii) achine learning (ML) - based metric engineering, and (iii) carefully chosen hierarchical clustering algorithms.
Gauge uses ML models to extract features of interest where analyst can quickly narrow down source of certain behavior in the collected data.
Launch Gauge
Input Data
Gauge works on both labeled and unlabeled data. Its flow consists of data parsing, feature selection, sanitization, and normalization, clustering, ML model training, and results visualization.
Algorithms
HDBSCAN hierarchy plus cluster visualizations, and SHAP - a game theoretic approach that can explain the output of black box machine learning models for model interpretations
Analysis
Analysis allows for further feature engineering and clustering technique refinements. It highlights the dominant correlations and negative correlations.
Visualization
Gauge has web-based and interactive that allows for real-time iterative domain-expert driven learning and clasification.