Tri-plots: scalable tools for multidimensional data mining

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 questions. We provide a set of rules on how to interpret a tri-plot, and we apply these rules on synthetic and real datasets. We also show how to use our tool for classification, when traditional methods (nearest neighbor, classification trees) may fail.

• Type: Conference
• Year: 2001
• Where: KDD
• Authors: Agma J. M. Traina, Caetano Traina Jr., Spiros Papadimitriou, Christos Faloutsos
• Keywords: Data Mining, KDD 2001, Multidimensional Datasets, Real Datasets, Smallest/largest Pair-wise Distance