Clustering of interval data based on city-block distances

The recording of interval data has become a common practice with the recent advances in database technologies. This paper introduces clustering methods for interval data based on the dynamic cluster algorithm. Two methods are considered: one with adaptive distances and the other without.

[1]  Mohamed A. Ismail,et al.  Fuzzy clustering for symbolic data , 1998, IEEE Trans. Fuzzy Syst..

[2]  K. Chidananda Gowda,et al.  Clustering of symbolic objects using gravitational approach , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Mohamed A. Ismail,et al.  On-line hierarchical clustering , 1998, Pattern Recognit. Lett..

[4]  K. Chidananda Gowda,et al.  Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity , 1995, Pattern Recognit. Lett..

[5]  M. Schader,et al.  New Approaches in Classification and Data Analysis , 1994 .

[6]  Francisco de A. T. de Carvalho,et al.  Proximity Coefficients between Boolean symbolic objects , 1994 .

[7]  Edwin Diday,et al.  Symbolic Cluster Analysis , 1989 .

[8]  Edwin Diday,et al.  Symbolic clustering using a new dissimilarity measure , 1991, Pattern Recognit..

[9]  Otto Optiz,et al.  Conceptual and Numerical Analysis of Data , 1989 .

[10]  Hans-Hermann Bock,et al.  Classification, Clustering, and Data Analysis , 2002 .

[11]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[12]  King-Sun Fu,et al.  Digital pattern recognition , 1976, Communication and cybernetics.

[13]  K. Chidananda Gowda,et al.  Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity , 1995, Pattern Recognit..

[14]  K. Chidananda Gowda,et al.  An ISODATA clustering procedure for symbolic objects using a distributed genetic algorithm , 1999, Pattern Recognit. Lett..

[15]  K. Chidananda Gowda,et al.  Symbolic clustering using a new similarity measure , 1992, IEEE Trans. Syst. Man Cybern..

[16]  Manabu Ichino,et al.  Generalized Minkowski metrics for mixed feature-type data analysis , 1994, IEEE Trans. Syst. Man Cybern..

[17]  A. Boudou,et al.  Mercury in the food web: accumulation and transfer mechanisms. , 1997, Metal ions in biological systems.

[18]  Allan D. Gordon,et al.  An Iterative Relocation Algorithm for Classifying Symbolic Data , 2000 .

[19]  Marie Chavent,et al.  A monothetic clustering method , 1998, Pattern Recognit. Lett..

[20]  Hans-Hermann Bock,et al.  Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data , 2000 .

[21]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[22]  Yves Lechevallier,et al.  Dynamical Clustering of Interval Data: Optimization of an Adequacy Criterion Based on Hausdorff Distance , 2002 .

[23]  Martin Schader,et al.  Data Analysis: Scientific Modeling And Practical Application , 2000 .