A Disjunctive Interpretation Approach to Missing Data Based on Clustering Quality

Data completion is a major challenge in many applications, particularly in Geographic Information Systems (GIS) for water networks. Numerous approaches have been proposed to address this problem, ranging from classical statistical methods to artificial intelligence-based techniques.

In this presentation, we address the problem of missing or imprecise data in water network GIS by proposing a clustering-based data completion approach. For a given attribute with missing or uncertain values, each possible value in the attribute domain is considered as a candidate for completion. Each candidate is evaluated by analyzing its impact on the clustering of the entire dataset: inserting a candidate value induces a specific global clustering, whose quality is assessed using appropriate clustering validity criteria. The value that yields the highest-quality clustering, namely the one that best captures the intrinsic structure of the data, is selected as the final completion value.

To cope with the combinatorial explosion resulting from multiple attributes with missing values and large domains, several strategies are employed to reduce the number of candidate completions, including aggregation mechanisms, while maintaining both the effectiveness and efficiency of the proposed approach.