The Cluster Structure Function

Abstract

For each partition of a data set into a given number of parts there is a partition such that every part is as much as possible a good model (an "algorithmic sufficient statistic") for the data in that part. Since this can be done for every number between one and the number of data, the result is a function, the cluster structure function. It maps the number of parts of a partition to values related to the deficiencies of being good models by the parts. Such a function starts with a value at least zero for no partition of the data set and descents to zero for the partition of the data set into singleton parts. The optimal clustering is the one selected by analyzing the cluster structure function. The theory behind the method is expressed in algorithmic information theory (Kolmogorov complexity). In practice the Kolmogorov complexities involved are approximated by a concrete compressor. We give examples using real data sets: the MNIST handwritten digits and the segmentation of real cells as used in stem cell research.

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“…The LDA algorithm can map a dataset to a new dimensional space where samples within the same category are separated by as little as possible, while data belonging to different categories are separated by large distances. This functionality allows for the classification of health and disease data [49]. In recent years, SVM, KNN, and PCA-LDA have been widely used in the interpretation of spectral data [25,[50][51][52][53].…”