QoS has been considered as a significant factor for web service marketing and selection. The interpretation of QoS value from web service consumers and providers would be very different. However, a large group of web service participants with different backgrounds may have difficulties in reaching consensus on the values of multi-dimensional web service QoS, so they may have to be clustered in multi-groups in order to improve effectiveness and efficiency. The similarity of clustered fuzzy QoS dispositions as well as their preference order over these attributes should be analyzed to form a multi-groups consensus framework. A soft multi-groups clustering approach could be adopted to prevent opinions from being excluded unintentionally. The group boundaries and similarity thresholds which are used for clustering and analyzing fuzzy QoS opinions can be moderated dynamically according to the feedback from the internal learning mechanism and the web service consumers. As a result, a model for marketing web services based on multi-group consumers' QoS consensus, the FMG-QCMA (Fuzzy Multi-Groups based QoS Consensus Moderation Approach), is proposed to meet the above requirements. The proposed FMG-QCMA is also evaluated through a case study to demonstrate its effectiveness and efficiency in relation to an existing framework, QCMA (QoS Consensus Moderation Approach). (C) 2010 Elsevier Inc. All rights reserved.
- Multi-attributes clustering; Similarity analysis