clusterswithAB

ClusterswithAB is a term used in data analysis to describe a family of clustering methods that partition data by jointly considering two feature groups, referred to as A and B. The goal is to produce clusters that reflect both the primary structure in A and supplementary structure in B, enabling more nuanced segmentation than single-view clustering.

Methodology and variants. The approach augments a standard clustering objective with a combined distance or similarity measure. A common formulation defines a composite distance D(x, x') = wA · dA(xA, x'A) + wB · dB(xB, x'B), where xA and xB are the projections of x onto A and B, and wA, wB are weights. Algorithms may use a two-stage training process or joint optimization, depending on the variant. Variants are often implemented as AB-kmeans, AB-DBSCAN, or AB-Spectral methods, with names varying by software package. The idea is to leverage information from both views to improve cluster coherence.

Applications and considerations. ClusterswithAB is applied in market segmentation, integrative biology (combining different omics or feature sets), sensor fusion, and anomaly detection where data arrive in two modalities. In practice, normalization, feature scaling, and careful choice of weights are important to balance the influence of A and B. Limitations include sensitivity to the alignment of views, increased computational cost, and potential interpretability challenges when clusters reflect complex cross-view interactions.

Example. In e-commerce, clustering users by demographics (A) and interaction history (B) can yield segments that better capture purchasing patterns than using either view alone.

See also. Clustering; multi-view clustering; two-view learning; AB testing.