Concept extraction and concept hierarchy construction based on cloud transformation

A method for extraction of qualitative concepts and construction of concept hierarchy is proposed. First, the cloud transformation is used to extract qualitative concept from database. Then, the former algorithm for pan concept tree generation is modified. When processing the cloud synthesization, the new algorithm takes the influence of the magnitude coefficient into consideration, which makes the output concept hierarchy more close to reality. As a case study, this method is used to extract qualitative concept and construct the concept hierarchy from car price data. Results demonstract that this method can extract qualitative concepts exactly. The concept center extracted by the proposed method is in good agreement with that obtained by conventional clustering algorithm. The proposed method can also describe the concepts with uncertainty, thus the ontology based on these concepts could give better specification of the conceptualization.