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Abstract: We focus on the problem of finding patterns across two large, multidimensional datasets. For example, given feature vectors of healthy and of non-healthy patients, we want to answer the following questions: Are the two clouds of points separable? What is the smallest/largest pair-wise distance across the two datasets? Which of the two clouds does a new point (feature vector) come from? We propose a new tool, the tri-plot, and its generalization, the pq-plot, which help us answer the above... (Update)
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BibTeX entry: (Update)
A. Traina, C. Traina, S. Papadimitriou, and C. Faloutsos. Tri-plots: Scalable tools for multidimensional data mining. In Proc. KDD 2001. http://citeseer.ist.psu.edu/traina01triplots.html More
@inproceedings{ traina01triplots,
author = "Agma J. M. Traina and Caetano Traina Jr. and Spiros Papadimitriou and Christos Faloutsos",
title = "Tri-plots: scalable tools for multidimensional data mining",
booktitle = "Knowledge Discovery and Data Mining",
pages = "184-193",
year = "2001",
url = "citeseer.ist.psu.edu/traina01triplots.html" }
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36 Spatial data mining: A database approach - Ester, Kriegel et al. - 1999
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34 Initialization of iterative refinement clustering algorithms - Fayyad, Reina et al. - 1998
34 correlation (context) - Belussi, Faloutsos et al. - 1995
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19 Algorithms for characterization and trend detection in spati.. (context) - Ester, Frommelt et al. - 1998
19 The pyramid-tree: Breaking the curse of dimensionality (context) - Berchtold, Bohm et al. - 1998
15 Mining databases - towards algorithms for knowledge discover.. (context) - Fayyad - 1998
14 Spatial join selectivity using power laws - Faloutsos, Seeger et al. - 2000
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