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Tri-Plots: Scalable Tools for Multidimensional Data Mining (2001)
| Content Provider | CiteSeerX |
|---|---|
| Author | Faloutsos, Christos Traina, Agma Traina, Caetano Papadimitriou, Spiros |
| 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 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. 1 |
| File Format | |
| Publisher Date | 2001-01-01 |
| Publisher Institution | Proc. of ACM KDD |
| Access Restriction | Open |
| Subject Keyword | Pair-wise Distance Multidimensional Datasets Traditional Method Feature Vector Multidimensional Data Mining Real Datasets Non-healthy Patient Scalable Tool New Point Classification Tree |
| Content Type | Text |
