| 摘要 |
A parallel processing computer system for clustering data points in continuous feature space by adaptively separating classes of patterns. The preferred embodiment for this massively parallel system includes preferably one computer processor per feature and requires a single a priori assumption of central tendency in the distributions defining the pattern classes. It advantageously exploits the presence of noise inherent in the data gathering to not only classify data points into clusters, but also measure the certainty of the classification for each data point, thereby identifying outliers and spurious data points. The system taught by the present invention is based upon the gaps between successive data values within single features. This single feature discrimination aspect is achieved by applying a minimax comparison involving gap lengths and locations of the largest and smallest gaps. Clustering may be performed in near-real-time on huge data spaces having unlimited numbers of features.
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