Modification of the Wu-Mendel approach for linguistic summarization

This paper presents versatile modification of the Wu-Mendel approach for linguistic summarisation and concentrates on eliminating the following main drawbacks of the approach: significant user/expert involvement, significant and rapidly increasing computational cost, some flaws in quality measures formulae. The techniques and formulae proposed to eliminate the aforesaid drawbacks are put through the experimental verification process involving several real-world datasets. The results of the experimental verification demonstrate the increased efficiency and effectiveness of the modified Wu-Mendel approach for linguistic summarisation.     

The fuzzification issue in the Wu–Mendel approach for linguistic summarisation using IF-THEN rules

A modification of the Wu–Mendel approach for linguistic summarisation (LS) of datasets is proposed in this paper. The proposed modification is a fuzzification-tuning technique that tunes the originally user defined specification of a fuzzy linguistic variable for each initial dataset attribute in the Wu–Mendel approach. The implication of the proposed technique in LS significantly decreases the complexity of linguistic summaries in terms of the number of rules and linguistic terms without the essential loss in accuracy, which is verified by carrying out the corresponding experimental analysis involving several real-world datasets.