Transfer entropy weighting soft subspace clustering

Abstract

In order to get better clustering precision, the traditional clustering algorithms usually need the support of large amount of historical data. The impact it brings about is: the previous clustering algorithm seems not effective if there exists some information losses in the current situation data collection and the division relationship between datasets is not significant. In this study, a novel clustering technique called transfer entropy weighting soft subspace clustering algorithm (T-EWSC) is proposed by employing the historical information. The properties of this algorithm are investigated and performance is evaluated experimentally using real datasets, including UCI benchmarking datasets, high dimensional gene expression datasets. The experimental results demonstrate that the proposed algorithm is able to use historical information to make up for the inadequacy of the current information and perform well.