Cluster analysis is an unsupervised machine learning technique that groups observations into homogeneous clusters based on similarity, without predefined categories. Used across marketing, healthcare, social sciences, and bioinformatics to reveal hidden patterns.
Our analysts select the most appropriate clustering algorithm, determine the optimal number of clusters using multiple criteria, and provide full cluster profiles with statistical validation.
Identify high-value customer segments using RFM (Recency, Frequency, Monetary) clustering for targeted strategies.
Cluster patients by clinical features, biomarkers, or symptom profiles to identify disease subtypes and treatment groups.
Hierarchical and K-Means clustering of gene expression profiles to identify co-expressed gene modules.
Group survey respondents into typologies based on attitudes, behaviours, or demographics for policy analysis.
Cluster financial instruments or portfolios by risk-return profiles, sector, or volatility patterns.
Identify student learning style clusters from assessment data to support personalised instruction design.
We use at least 3 methods (elbow, silhouette, Gap statistic) to confirm the optimal cluster number not just one heuristic.
Every cluster described with means, standard deviations, and key distinguishing variables with ANOVA confirmation of cluster distinctiveness.
Dendrograms, scatter plots, radar/spider charts, and heatmaps delivered at 300 DPI ready for journal figures.
Cluster naming and interpretation written by domain experts in your field not just statistical output.
Variables standardised (Z-scores), outliers detected and handled, missing data imputed, and clustering assumptions verified.
Hierarchical (for small samples), K-Means (large samples), Two-Step (mixed data), or model-based clustering selected based on data type and research objective.
Elbow method, silhouette analysis, Gap statistic, and dendrogram inspection used together to identify the most stable number of clusters.
Each cluster described using means, frequencies, and discriminating variables. ANOVA or chi-square tests confirm cluster distinctiveness.
Internal validation (Dunn index, Davies-Bouldin), external validation, and cluster visualisations (scatter plots, heatmaps, dendrograms) produced.
Each cluster labelled and described in plain language for your results chapter or journal manuscript.