Computing the minimum Hausdorff distance between two point sets on a line under translation
Information Processing Letters
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
ACM Computing Surveys (CSUR)
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Fuzzy c-means clustering methods for symbolic interval data
Pattern Recognition Letters
Centre and Range method for fitting a linear regression model to symbolic interval data
Computational Statistics & Data Analysis
Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
Descriptive statistics of non-uniform interval symbolic data
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Clustering constrained symbolic data
Pattern Recognition Letters
Constrained linear regression models for symbolic interval-valued variables
Computational Statistics & Data Analysis
Clustering of symbolic data using the assignment-prototype algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Unsupervised pattern recognition models for mixed feature-type symbolic data
Pattern Recognition Letters
Dynamic clustering of interval-valued data based on adaptive quadratic distances
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A partitioning method for mixed feature-type symbolic data using a squared Euclidean distance
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Interval competitive agglomeration clustering algorithm
Expert Systems with Applications: An International Journal
Similarity-margin based feature selection for symbolic interval data
Pattern Recognition Letters
Measure based metrics for aggregated data
Intelligent Data Analysis
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Standardization of interval symbolic data based on the empirical descriptive statistics
Computational Statistics & Data Analysis
Self-organizing map for symbolic data
Fuzzy Sets and Systems
Fuzzy Kohonen clustering networks for interval data
Neurocomputing
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
Using weighted clustering and symbolic data to evaluate institutes’s scientific production
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Clustering interval data through kernel-induced feature space
Journal of Intelligent Information Systems
Clustering interval-valued data using an overlapped interval divergence
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
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This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocation algorithms involving the construction of the clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between the clusters and their corresponding representatives. In this paper, each pattern is represented by a vector of intervals. Adaptive Hausdorff distances are the measures used to compare two interval vectors. Adaptive distances at each iteration change for each cluster according to its intra-class structure. The advantage of these adaptive distances is that the clustering algorithm is able to recognize clusters of different shapes and sizes. To evaluate this method, experiments with real and synthetic interval data sets were performed. The evaluation is based on an external cluster validity index (corrected Rand index) in a framework of a Monte Carlo experiment with 100 replications. These experiments showed the usefulness of the proposed method.