Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
Pattern Recognition Letters
Comparison of fuzzy numbers using a fuzzy distance measure
Fuzzy Sets and Systems - Fuzzy intervals
Dynamic clustering for interval data based on L2 distance
Computational Statistics
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
Dynamic clustering of interval-valued data based on adaptive quadratic distances
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fuzzy case based reasoning approach to value engineering
Expert Systems with Applications: An International Journal
Measure based metrics for aggregated data
Intelligent Data Analysis
Standardization of interval symbolic data based on the empirical descriptive statistics
Computational Statistics & Data Analysis
Extracting temporal patterns from interval-based sequences
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Self-organizing map for symbolic data
Fuzzy Sets and Systems
Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices
Fuzzy Sets and Systems
A Fuzzy Clustering Model for Fuzzy Data with Outliers
International Journal of Fuzzy System Applications
A hierarchical modeling approach for clustering probability density functions
Computational Statistics & Data Analysis
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Interval data allow statistical units to be described by means of intervals of values, whereas their representation by means of a single value appears to be too reductive or inconsistent. In the present paper, we present a Wasserstein-based distance for interval data, and we show its interesting properties in the context of clustering techniques. We show that the proposed distance generalizes a wide set of distances proposed for interval data by different approaches or in different contexts of analysis. An application on real data is performed to illustrate the impact of using different metrics and the proposed one using a dynamic clustering algorithm.