| Summary: | 碩士 === 中原大學 === 工業與系統工程研究所 === 103 === With the popularity of smart devices and the variety of built-in sensors, context-awareness applications were broadly developed to fulfill different requirements. The application programs deal with various data and tasks, therefore, optimizing context-awareness systems would provide better services for users. This research considers a consensus decision making process and recognizes few users’ extreme preferences would make the direction process off the right track. This research proposed to develop a consensus decision support system and it would help users quickly find acceptable results for the majority of users. When users discuss consensus decision with the context awareness system, this research uses a clustering method to group different opinions and to identify which opinions are more acceptable. This method would improve the decision making problem caused by extreme preferences of few users.
First, a context aware framework was developed and users’ preference was noted as a real number between 0 and 1, in which 0 represents not like, 1 represents like, and 0.5 is no comment. Next a consensus model and a K-means consensus model were developed. This research used data mining software to group preference data, and the centroid of a group represents the opinion of that group. If there was more than one group that had the same centroid, then the preference of that centroid was determined by the numbers of the groups. F-measure was used as an index to compare the performance of the proposed model and human judgment. And the Xie-Beni index was applied to find the grouping number with higher accuracy.
This study used finding a common dining restaurant as an example to illustrate the proposed model. The experimental results showed the proposed K-means consensus model could improve the decision offset problem caused by extreme preferences of few users. When grouping numbers are larger than 5, K-means consensus model would be more accurate than consensus model in this case. The more of the group number, the larger of the Xie Beni index would be. In this experiment, the results of the K-means consensus model with Xie Beni index between 100 and 1000 could be more accurate than the results of the consensus model. However, if Xie Beni index was larger than 1000, decisions could be wrong and the reason could be the scattering data.
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