Title :
Clustering symbolic interval data based on a single adaptive hausdorff distance
Author :
De Carvalho, Francisco A T de ; Pimentel, Julio T. ; Bezerra, Lucas X T ; De Souza, Renata M C R
Author_Institution :
Clustering Univ. of Pernambuco Recife, Recife
Abstract :
The recording of symbolic interval data has become popular with the recent advances in database technologies. This paper introduces a dynamic clustering method to partitioning symbolic interval data. This method furnishes a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare symbolic interval data, the method uses a single adaptive Hausdorff distance that at each iteration changes but is the same for all the clusters. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.
Keywords :
database theory; pattern clustering; adaptive Hausdorff distance; database; dynamic clustering method; symbolic interval data clustering; Clustering algorithms; Clustering methods; Data analysis; Data mining; Heuristic algorithms; Iterative algorithms; Optimization methods; Partitioning algorithms; Pattern analysis; Prototypes;
Conference_Titel :
Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
978-1-4244-0990-7
Electronic_ISBN :
978-1-4244-0991-4
DOI :
10.1109/ICSMC.2007.4413616
