Key data for cloud computing based on ensemble clustering approximate analysis

Abstract

To realise multi-label classification of text and meanwhile reduce calculation complexity and keep classification precision, dimensionality-reduction clustering method for fuzzy association of text multi-label based on cluster classification has been proposed. In text classification, it usually involves enormous feature numbers, which may cause curse of dimensionality. In addition, classification region can not always keep convex characteristics. It can be non-convex region composed of several overlapping or intersecting sub-regions. Above mentioned automatic classification system may require enormous memory requirement or has poor classification performance. Hence, new multi-label text classification method is proposed to overcome these problems in combination with fuzzy association technology. Fuzzy association evaluation is adopted to transform high-dimension text to low-dimension fuzzy association vector, thus avoiding curse of dimensionality. Experiment results show that the proposed method can more effectively classify text multi-label problem.