# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Rule/Logical Pattern Mining
#### 6.5.1 Technical and Methodological Insights  
The Rule/Logical Pattern Mining class represents a cohesive paradigm where the interpretability of concept drift is intrinsically woven into the very fabric of the model's evolution. Across all approaches in this category, drift is not merely detected but explained through dynamic, human-readable logical structures--primarily IF-THEN rules, feature sets, or temporal clauses--that visibly morph in response to changing data distributions. This transforms adaptation into a self-documenting narrative: practitioners observe drift not through abstract error metrics or detached alarms, but through direct, tangible changes in the interpretable components that constitute the model's reasoning. The unifying thread is that rules and features serve dual purposes: they are both predictive instruments and the primary artifacts through which drift is communicated. When the underlying concept shifts, these elements adapt--spawning new clauses, pruning obsolete conditions, sliding temporal boundaries, or reweighting inputs--and these adaptations are the explanation. Operators thus grasp how, where, and why the concept has changed by inspecting the evolving logic itself, bypassing the need for statistical intermediaries. Common to all papers is the seamless integration of drift detection and explanation. Drift manifestsas a visible evolution in the model's interpretable constructs, ensuring explanation occurs at the moment of adaptation rather than as a post hoc analysis. For instance, in X-Fuzz [106], rising predictive error directly triggers neuro-fuzzy rule updates, and each reshaped IF-THEN hyperplane is immediately exposed to the user, explicitly highlighting which variables survived the change. Simultaneously, LIME-based bar charts--validated for faithfulness via feature-masking correlation--localize the impact of input shifts at the instance level. Similarly, [PERSON] et al. 's [132] fraud detection system makes drift intelligible through a shifting balance of transparent business rules and surrogate \"scoring rules\": a rising proportion of alerts lacking rule support directly signals that fraud tactics have outpaced the current vocabulary, while each new scoring rule flags emerging patterns for investigation. This tight coupling ensures that the signal of drift (e.g., rising error) and its explanation (e.g., new clauses, vanishing features) are unified, eliminating interpretive latency. Human-centric presentation further defines this class. Rather than relying on technical scores, explanations prioritize actionable insights grounded in domain semantics. OTARL [110], for example, encodes drift in temporal association rules built from multivariate shapelets. As distributions shift, operators literally see shapelets slide along a timeline or temporal relations flip (e.g., from FEN to NOV), enabling them to articulate shifts like \"the second variable now lags the first\" without consulting error curves. In [PERSON] et al. 's [95] Attribute-based Decision Graphs (AbDGs), decaying vertex weights cause attributes to drop in rule-clause rankings or vanish entirely, creating an immediate visual narrative of feature obsolescence. [PERSON] et al. [30] distill drift into plain-language sentences (e.g., \"Decision switched because Cost = High\") derived from top-k features, while FGRI [134] uses the appearance or disappearance of IF-THEN conjunctions (e.g., pressure threshold clauses) that mirror plant-floor terminology. Visual anchors--color-coded SHAP bars in DMI-LS, tagged N-gram lists in DAEMON [113], or evolving rule timelines in ERulesD2S [99]--replace opaque metrics as the primary interface for operators. Studies report practitioners relying overwhelmingly on these artifacts (e.g., 42% of decisions in X-Fuzz; retraining approvals via SHAP rankings in DMI-LS), underscoring their efficacy in conveying drift narratives. Within this shared foundation, key innovations refine how interpretability is achieved.