Unsupervised Learning Workflow

• Unsupervised learning workflow is a systematic process that applies machine learning techniques to unlabeled data to reveal hidden patterns and structures.
• It involves a sequence of stages including precise question formulation, rigorous data preparation, modeling with multiple algorithms, and comprehensive validation.
• Practical applications span fields like climate science and astronomy, where robust validation methods such as ARI ensure reliable and reproducible discoveries.

Unsupervised learning workflow refers to the systematic process of applying machine learning techniques to unlabeled data in order to discover patterns, structures, or relationships that can be utilized for scientific discovery, data understanding, or downstream analysis. Such workflows have been instrumental in domains like climate science, biomedicine, astronomy, and chemistry, where labeled data is scarce and the goal is often to yield new knowledge rather than optimize for a predefined target. Establishing robust unsupervised learning workflows is essential for producing reliable, valid, and reproducible scientific discoveries, as arbitrary analytic decisions and lack of validation can otherwise undermine confidence in these findings (2506.04553).

1. Stages of the Structured Unsupervised Learning Workflow

A structured workflow for scientific unsupervised learning is model-agnostic and comprises the following stages:

1. Formulate Validatable Scientific Question The process begins with in-depth literature review and consultation with domain experts to identify actionable, quantifiable, and data-aligned scientific goals. The selected question should be precisely defined such that it can be validated empirically within the data constraints.
2. Data Preparation and Exploration This stage encompasses data collection and planning of features, explicit handling of confounders, splitting datasets into training and testing sets prior to any processing, comprehensive cleaning (including outlier removal), feature engineering, and systematic imputation for missing data. Exploratory Data Analysis (EDA) is performed using both univariate and multivariate visualizations to understand marginal distributions, potential batch effects, and key multivariate relationships.
3. Modeling Multiple unsupervised learning methods (e.g., clustering, dimensionality reduction) are applied, with systematic exploration of model and preprocessing alternatives, as well as hyperparameter grids. Discoveries robust across methodological choices are prioritized for further investigation.
4. Validation Rigorous validation is crucial and consists of assessing both stability (how robust findings are to subsampling, random initializations, and preprocessing pipelines) and generalizability (whether structures identified in training data hold in test or new datasets). These criteria are used for model and parameter selection.
5. Communication and Documentation This stage involves ongoing communication with scientific collaborators and transparent documentation of all analysis steps, modeling choices, code, and results. Reproducibility is ensured via sharing of data splits, code, and comprehensive analysis records.

2. Best Practices for Workflow Stages

• Formulating Questions: Scientific questions must be explicit, quantifiable, and matched to the available data and features. Example: "Are there phenotypic subgroups among disease cases, and how do they differ on features X, Y, Z?" Rather than exploratory questions with ambiguous or untestable outcomes.
• Data Preparation: Fix training/test splits before additional processing. Employ multiple imputation strategies to address missing data. Construct and document alternate preprocessing pipelines, as even minor changes (e.g., imputation method, normalization) can alter unsupervised results significantly.
• Exploration: Visualize individual features, correlations, and data structure using several methods (including multidimensional and high-dimensional projections). Stochastic visualization tools (e.g., UMAP, t-SNE) should be run multiple times to check for robustness of observed structure.
• Modeling: Apply a range of algorithms (for example, various clustering algorithms and dimensionality reductions) and variations over a hyperparameter grid. True scientific structure should be replicable under a range of methods and parameter settings.
• Validation: Quantify both (i) stability—repeatability of findings across subsampling/preprocessing/model alternates, and (ii) generalizability—prediction of discovered structure (e.g., cluster assignments) in new data, commonly via supervised classifiers trained on unsupervised labels.
• Communication: Share visualizations and model results at every stage with all stakeholders. Fully document each step of the analysis. Share code, splits, and necessary artifacts to enable end-to-end reproducibility.

3. Case Study: Astronomy—Identification of Chemically Homogeneous Stellar Groups

The workflow is exemplified via the analysis of Milky Way globular clusters using APOGEE chemical abundance data (>3,000 stars, 19 elements). The scientific objective was to discover subgroups, possibly with shared formation history, beyond conventional spatial proximity-based globular cluster definitions.

• Multiple preprocessing pipelines were established, including alternate quality control and imputation choices. An 80/20 train/test split was used.
• Dimensionality reduction was performed with PCA, t-SNE, and UMAP, with the neighborhood retention metric employed to select an embedding that best preserves local data structure. The metric is defined as

$$\text{Retention} = \frac{1}{n} \sum_{i=1}^n \frac{|N_{k,\text{orig}}(i) \cap N_{k,\text{embed}}(i)|}{k}$$

where $N_k$ refers to the $k$ nearest neighbors in the original or embedded space.

• Clustering was performed using multiple algorithms and parameter grids, with consensus labels and stability metrics based on the Adjusted Rand Index (ARI):

$$ARI(C_1, C_2) = \frac{\text{Index} - \text{Expected Index}}{\text{Max Index} - \text{Expected Index}}$$

• Model selection and scientific interpretation were driven by high local stability (frequency with which pairs of stars clustered together across 100+ runs) and strong test-set generalizability (ability to predict clusters in held-out data by learning a mapping from features to cluster labels).

The resulting groupings both recapitulated known iron-rich/poor populations and yielded finer structure within clusters. Only clusters stable to all pipeline choices and generalizable to new data were deemed suitable for scientific analysis.

4. Validation: Methods for Robustness and Generalizability

• Consensus Clustering: Aggregates cluster assignments from multiple runs/pipelines into a co-clustering matrix, where the entry $(i,j)$ denotes how often points $i$ and $j$ were found in the same cluster.
• Adjusted Rand Index (ARI): Quantifies agreement between different clusterings across subsamples or model choices.
• Stability Metric for Model Selection:

$$S(k, m, b) = ARI(C_1[sub_1 \cap sub_2], C_2[sub_1 \cap sub_2])$$

The method/parameter $m^*, k^*$ maximizing average stability across $B$ runs is selected:

$$m^*, k^* = \arg\max_{k, m} \frac{1}{B} \sum_{b=1}^B S(k, m, b)$$

• Test Set Generalizability: A classifier is trained to predict cluster assignments on the training set and evaluated on the test set. High classification accuracy or ARI suggests that the clusters reflect robust, generalizable structure.

5. Reproducibility and Communication

Robust scientific insights from unsupervised learning require full transparency and reproducibility. Recommendations for ensuring reliability include:

• Every preprocessing, feature engineering, and model selection step is documented and justified.
• Deterministic code and pipelines are shared, and stochastic pipelines are run with enough repeats to quantify variability.
• All results—both substantive scientific findings and associated robustness metrics—are communicated to collaborators and, upon publication, to the broader community using open-source code/data supplements.
• The requirement is that identical code, inputs, and deterministic algorithms should yield identical results, while repeated runs with stochastic elements produce consistent conclusions.

6. Summary Table: Stages, Best Practices, and Validation

Stage	Best Practices	Validation/Documentation
Formulate Question	Precise, actionable, aligns with data	Documented, defined before data analysis
Data Preparation	Plan splits/features; multiple pipelines; imputation	Sensitivity analysis; code and choices saved
Data Exploration	Multimodal, high-dim visualization; outlier checks	Repeated visualization; documented output
Modeling	Apply multiple algorithms/hyperparameter grids	Report all runs; compare via summary metrics
Validation	Quantify stability/generalizability (ARI/consensus)	Report metrics per cluster/item/model
Communication	Share code, splits, all steps, robustness analyses	Open-source supplement; published method logs

7. Conclusion

The outlined workflow and best practices offer a principled blueprint for using unsupervised learning in scientific discovery. By emphasizing validation, robustness, and reproducibility at every stage—from problem formulation through data preparation, modeling, validation, and publication—these recommendations address the arbitrariness and fragility inherent to unsupervised analysis. The result is a framework that both enables new scientific findings and ensures those results are credible and shareable within the scientific community (2506.04553).