# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Prototype/Medoid and Exemplar Tracking
#### 6.9.1 Technical and Methodological Insights  
Here is a seamlessly integrated narrative that weaves together the explanatory approaches of all papers into a continuous analytical discussion: the Prototype/Medoid and Exemplar Tracking approaches collectively transform concept drift from an abstract statistical phenomenon into a visually interpretable narrative by anchoring explanation to the evolutionary journey of representative data points. This foundational principle manifests differently across studies while maintaining a coherent explanatory philosophy. [PERSON] and [PERSON] (2014) establish the paradigm by demonstrating how prototype movement in input space intrinsically encodes the drift explanation--tangent arrows quantifiably point to shifting feature importance when benign packets grow larger and less regular, making displacement vectors themselves attribution maps. The SECLEDS approach introduced by [PERSON] and [PERSON] evolves this geometric storytelling, making drift democratically visible through medoid popularity contests (2015). Rather than sliding points, SECLEDS tracks vote-share histograms and exemplar turnover, allowing operators to compare the concrete packet trace of an evicted \"yesterday's behavior\" against the newly promoted pattern now deemed central--a narrative of representation succession where three consecutive medoid swaps tell the story of changing data regimes. Building on this visual accessibility, the Interactive-COSMO method introduced by [PERSON] et al. distills drift interpretation into an even more immediate spatial language (2015). By coupling a migrating prototype with a \"breathing\" decision radius and highlighted boundary crossings, the system creates a real-time cartography of normality: when a pump's prototype slides toward higher loads while the radius simultaneously widens, engineers immediately grasp both the new operational center and growing uncertainty without statistical abstractions. This spatial coherence is refined further by the ICICLE approach proposed by [PERSON], which tackles the challenge of preserving narrative continuity during incremental drift (2016). Where earlier methods might show disjointed shifts, ICICLE's similarity-distillation regularizer ensures prototypes like \"bird beaks\" migrate cohesively across tasks--new beak concepts anchor near old ones rather than appearing randomly in latent space--while logit-bias compensation keeps prior prototypes active. Clinicians thus witness controlled visual evolution rather than disruptive rewiring, maintaining interpretability itself against drift. [PERSON] et al. complete this explanatory arc by transposing geometric movements into domain-specific analogies. Financial drift materializes through dynamically weighted neighbor lists, where retail banks yielding to logistics startups in precedent rankings explicitly narrate shifting risk landscapes. Crucially, all methods share a core explanatory mechanism: whether through [PERSON]'s sliding prototypes, SECLEDS' medoid elections, COSMO's migrating centroids, ICICLE's anchored features, or Sun's rotating precedents, the evolving representative is the explanation. Later systems enhance this by ensuring narrative continuity--ICICLE's task-to-task tethering and Sun's fading weights prevent jarring context breaks that might obscure drift causality. Collectively, they prove that representative tracking bypasses technical metrics entirely, translating drift into operator-actionable stories where motion, succession, or recomposition directly reveal both what changed and why. This evolutionary thread--from individual point movement to contextualized representative lifecycles--forms the class's distinctive contribution: making drift interpretable through embodied data narratives. Therefore, this class is aligned with XAI.