運用K-means改善情境感知框架中共識模型之決策結果

Application of K-means method to improve decision making results of a consensus model in a context awareness framework

曾鈺棠(Yu-Tang Tseng)

指導教授 ： 項衛中

中原大學/電機資訊學院/工業與系統工程研究所/碩士(2015年)

https://doi.org/10.6840/cycu201500693

摘要

隨著智慧型裝置的普及化與內建感應器的多樣化，情境感知應用程式也大量的發展以滿足不同的需求，情境感知系統必須處理更多樣的資料，因此優化系統將提供更好的服務。本研究考慮共識決策會因為少數人的極端意見，造成決策方向的偏移，因此開發共識決策支援系統，幫助使用者們快速得到大多數使用者可以接受的結果。使用者在情境感知系統中進行共識討論時，本研究利用分群的方式將不同的意見分類，藉此分析出具有較多認同的意見，來改善因為少數人的極端意見造成決策方向偏移之問題。
首先建立情境感知框架，使用者偏好以0到1之間的實數來表示，0代表不喜歡與1代表喜歡，0.5代表沒意見，再建立共識模型與K-means共識模型。本研究利用探勘程式將資料分群，利用群組質心代表群組偏好，如果不同群組具有相同的質心則以意見數較多者代表全體的共識。本研究以F-measure為指標，將程式結果和人為判斷結果相互比較，分析模型的準確度，並以Xie-Beni指標分析分群的數量，以獲得較高的準確率。
本研究以選擇餐廳為範例來驗證所提的模型，實驗結果顯示K-means共識模型可以改善被少數極端意見左右決策之問題，當分群數量大於5群時，K-means共識模型的準確率會優於共識模型，且分群數量越大Xie-Beni指數也會越大。依據本實驗結果，Xie-Beni指標集中程度介於100至1000時， K-means共識模型的F-measure值會有機會優於共識模型，但是大於1000時則會出現誤判的情形，原因可能是資料不夠集中。

關鍵字

共識模型 ； K- means ； 情境感知

並列摘要

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.

並列關鍵字

Consensus Model ； K- means ； Context Awareness

參考文獻

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