TL;DR
The paper introduces the truematch algorithm for cluster matching, which improves stability and accuracy by using a chi-square transformation and probabilistic tie-breaking, enhancing cluster validation and ensemble methods.
Contribution
It presents a novel cluster matching algorithm that addresses limitations of classic methods by incorporating chi-square transformation and randomness, scalable for practical use.
Findings
More consistent results for unequal cluster sizes
Improved stability over classic methods
Available as free R software
Abstract
Cluster matching by permuting cluster labels is important in many clustering contexts such as cluster validation and cluster ensemble techniques. The classic approach is to minimize the euclidean distance between two cluster solutions which induces inappropriate stability in certain settings. Therefore, we present the truematch algorithm that introduces two improvements best explained in the crisp case. First, instead of maximizing the trace of the cluster crosstable, we propose to maximize a chi-square transformation of this crosstable. Thus, the trace will not be dominated by the cells with the largest counts but by the cells with the most non-random observations, taking into account the marginals. Second, we suggest a probabilistic component in order to break ties and to make the matching algorithm truly random on random data. The truematch algorithm is designed as a building block…
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Taxonomy
TopicsAdvanced Clustering Algorithms Research · Bayesian Methods and Mixture Models · Data Management and Algorithms