Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
Dynamic clustering for interval data based on L2 distance
Computational Statistics
New clustering methods for interval data
Computational Statistics
Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics)
Measure Theory and Probability Theory (Springer Texts in Statistics)
Measure Theory and Probability Theory (Springer Texts in Statistics)
Internal quality measures for clustering in metric spaces
International Journal of Business Intelligence and Data Mining
Dynamic clustering of interval data using a Wasserstein-based distance
Pattern Recognition Letters
Histogram based segmentation using Wasserstein distances
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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Aggregated data arises commonly from surveys and censuses where groups of individuals are studied as coherent entities. The aggregated data can take many forms including sets, intervals, distributions and histograms. The data analyst needs to measure the similarity between such aggregated data items and a range of metrics are reported in the literature to achieve this (e.g. the Jaccard metric for sets and the Wasserstein metric for histograms). In this paper, a unifying theory based on measure theory is developed that establishes not only that known metrics are essentially similar but also suggests new metrics.