Multi-attribute Complex Huge Group-decision Methods Research Basing On Clustering Algorithm

As a new development trend in decision field, the MCHGD (Multi-attribute Complex Huge Group-decision) is accounted by Theory and Business Career, it urgently demands proper decision methods to support the decision processes. Therefore, this thesis will study the methods of MCHGD basing on the clustering algorithm in a different light.According to the problems of group-complexity such as the influence of decision results imposed by complex group-behaviors, the verifying of the weight of alternative group-decision evaluation criteria, and the group-consistency amendment of individual' s divergence of their owns, in addition, the problem of huge group's high efficiency aggregation considering complex group characteristics, this thesis outlines the improved MFCM (Minimum Fuzzy c-means) basing on the Graph Theory, and proposes the huge group-decision method with the group-complexity basing on MFCM, the method of group-consistency amendment basing on MFCM and WHCM (Weighted Hard C-means). In the mean time, the thesis presents the framework of the MCHGDSS (Multi-attribute Complex Huge Group-decision Support System) basing on Multi-agent and DW (Data Warehouse), develops the MCHGDSS basing on the clustering algorithm among it. At last, a case research of the group-consume behavior decision of Chinese consumer in network economic environment is performed.Main contents of this thesis are outlined as follows:(1) An improved clustering algorithm and the efficient huge group-decision theory and method with complex group-behavior basing on the algorithm. To combine the group-thinking interaction decision with the efficient huge group-integration decision, the thesis points out a kind of 2-phase group-decision theory and method. First, to deal with the problems of decision quality caused by many kinds of complex group-behaviors, the thesis designs 5-phase development model of group-thinking, and the coordinating mechanism of multi-dimension supervised by the group-leaders which is introduced to control the process of group-thinking interaction. In the mean time, to describe the relations among the group-member, the thesis views the group-member as a picture, representing their relations by matrix adjacency, the democratic leaders' set is also produced by MFCM. And then the thesis aggregates the huge group efficiently using MFCM continuously, defines group-preference vector and group-consistency index. The result of MCHGD is put forward after getting the weight of alternative group-decision criteria using entropy method. The validity and accuracy of the method is verified by the computer simulation and comparing analysis.As the technical basis of the above method, an improved MFCM is presented to support the MCHGD. To answer the questions of the existing fuzzy clustering algorithms involving local limit value, bad scalability and only for the statistic data, this thesis puts forward a new kind of MFCM basing on MCDSA (Minimum Connected Donating Set Algorithm) which clusters only the dominating points to improve the traditional FCM from the view of overall.The improved MFCM could be used to aggregate the group, on the other hand, it also could be used to particular describe the influence of the complex group-behaviors from the point of correlation and control.(2) A huge group-consistency amendment method with learning ability basing on clustering. According to a certain decision project, the thesis puts forward a group-consistency amendment method basing on a kind of optimized c-means clustering algorithm, aiming at lager scale group, and considering the ability of evolution by learning. First, the thesis regards the group-consistency amendment is an evolutionary process and designs the evolutionary program of the group-member. Then, by using the tightness relations of the group-comparability and the group-consistency, a gradual decision method of amendment group-consistency by optimizing attributes' weight is presented to avoid the fault caused by individual idea divergence. Lastly, the method is also validated by the computer simulation and comparing analysis. The above efficient huge group-decision theory and method with complex group-behavior could be used to make decision after the consistency amendment.(3) A new DW associational operator model is brought forward. Because the MCHGDSS needs the general and assemble information to support its distribution, the thesis extends the DW analysis algebra model advanced by Datta, realizes the operations among multiple multi-dimensional cubes by adding a kind of new associational operator.