clustersfollowed

Clustersfollowed is a concept in data analysis that denotes the sequence of cluster labels observed for an entity over time or across events. It emphasizes the order and transitions between clusters, providing a temporal view of how data groups evolve or how a user moves through a space of topics or behaviors. It is used to distinguish dynamic trajectories from static cluster assignments.

Computation involves assigning each time point or event to a cluster using a chosen algorithm, then recording the resulting labels in order. The trajectory can be represented as a sequence of labels, and can be enriched with transition counts, dwell times, or a Markov-style transition matrix. Variants may aggregate clusters into macro-states to reduce noise.

Applications include user behavior analysis, recommender systems, workflow optimization, and anomaly detection. For example, a visitor's clustersfollowed path might be A → B → C, revealing shifts in interest or behavior. In process monitoring, common trajectories can be identified and compared across sessions.

Limitations include sensitivity to clustering method, the number of clusters, and temporal granularity. Noisy data or unstable clusters can produce misleading trajectories, so validation and robustness checks are important. Interpretability depends on the semantic clarity of the cluster labels.

Related concepts include clustering, sequence mining, Markov models, and time-series analysis.