利用分割式分群演算法找共識群解群體決策問題

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姓名

陳娃妏(Wa-wun Chen) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

利用分割式分群演算法找共識群解群體決策問題
(Find Consensus Cluster by Partitioning Clustering for Group Ranking Problem)

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摘要(中)

群體決策問題在最近幾年中，因為被應用到多種的領域上而備受注目。群體決策問題主要是從收集到的使用者喜好資料當中，找出共識的序列代表這些使用者的喜好以利決策者做決策。在過去的研究中，有完整排名序列及最大共識序列兩種輸出格式，兩者各有其優點而被廣泛採用，然而完整排名序列雖然藉由匯總使用者喜好資料的方式，建立了一組所有項目的序列結果，但是沒有考慮到有多少喜好衝突存在。另一方面，最大共識序列克服了完整排名序列的缺點，提高了結果的共識程度，也指出喜好衝突的部份待作進一步分析，但是零散的序列結果並不容易被理解及使用。為改善以上兩者的缺點，我們嘗試利用對使用者喜好排名序列分群的方式，找出所有項目的共識群。共識群的優點是群中可以容忍喜好衝突存在，而且結果也容易被理解及使用。我們的研究定義了兩個群定義，透過分割式分群演算法的k-mean及k-medoids的概念進行兩個階段的分群。最後，我們透過不同參數設定進行實驗分析並作討論及總結。

摘要(英)

The group ranking problem is used to obtain the coherent result from users’ preference data and has received increased attention due to its widespread applications in recent decades. Among previous researches, two output formats have their own advantage and have been used in many applications. Nevertheless, total ranking list consolidates a consensus ordering list by aggregating users’ preference data regardless of how many conflicts exist. On the other hand, maximum consensus sequence has overcome the degree of majority and identified the conflicts that need further negotiation. However, the results of maximum consensus sequence are fragmented, and not easy to understand and use. To conquer these disadvantages, we try a novel way called consensus clusters to cluster user ranking lists. According to two cluster definitions we defined, our methodology is developed based on k-means and the concept of its variant k-medoids to minimize the cost functions. These consensus clusters tolerates the conflicts of item preference and is easy to understand. At the end, we discuss our experimental results and contributions.

關鍵字(中)

★ 群體決策問題
★ K-medoids
★ K-均值

關鍵字(英)

★ Keywords: K-mean
★ K-medoids
★ Group ranking problem
★ Ranking list

論文目次

Abstract ii
致謝 iv
Contents v
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
　1.1 Background 1
　1.2 Motivation 1
　1.3 Research Objectives 2
　1.4 Thesis Framework 3
Chapter 2 Related Works 4
　2.1 Group ranking problem 4
　2.2 Clustering methods 5
Chapter 3 Problem definition 8
Chapter 4 Methodology 13
　Phase 0. Data Transformation (Procedure Data_Trans) 14
　Phase 1. First clustering stage (Procedure Dist_Cluster) 15
　Phase 2. Second clustering stage (Procedure Cost_Cluster) 16
Chapter 5 Experiments 19
　5.1 Synthetic data 19
　　5.1.1 Run time comparisons 20
　　5.1.2 Convergence comparisons 22
　　5.1.3 Cost comparisons 33
　5.2 Real data 35
　　5.2.1 Activity dataset 35
　　5.2.2 Journal dataset 39
Chapter 6 Conclusions and Future Works 44
Reference 45
Appendix A 47
Appendix B 48

參考文獻

[1] Cook, W.D., 2006, “Distance-based and ad hoc consensus models in ordinal preference ranking,” European Journal of Operational Research, Vol. 172, No.2, pp. 369–385.
[2] Cook, W.D., Golany, B., Kress, M., Penn, M., and Raviv, T., 2005, “Optimal allocation of proposals to reviewers to facilitate effective ranking,” Management Science, Vol.51, No.4, pp. 655–661.
[3] Cook, W.D., Golany, B., Kress, M., Penn, M., and Raviv, T., 2007, “Creating a consensus ranking of proposals from reviewer’s partial ordinal rankings,” Computers and OR, Vol.34, No.4, pp. 954–965.
[4] Chen, Y.L. and Cheng, L.C., 2008, “A novel collaborative filtering approach for recommending ranked items,” Expert Systems with Applications, Vol.34, No.4, pp. 2396–2405.
[5] Fagin, R., Kumar, R., and Sivakumar, D., 2003, “Efficient similarity search and classification via rank aggregation,” Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM, San Diego, California.
[6] Beg, M.M.S. and Ahmad, N., 2003, “Soft computing techniques for rank aggregation on the World Wide Web,” World Wide Web-Internet and Web Information Systems, Vol.6, No.1, pp. 5–22.
[7] Cohen, W., 1999, “Learning to order things,” The Journal of Artificial Intelligence Research, Vol.10, pp. 43.
[8] Borda, J.C., 1981, “Memoire sur les elections au scrutin,” Histoire de l Academie Royale de Science, Paris.
[9] Kendall, M.,1955, Rank Correlation Methods, Hafner, New York.
[10] Kemeny, J.G. and Snell, L.J., 1962, “Preference ranking: An axiomatic approach,” Proceedings of Mathematical Models in the Social Science, pp. 9–23.
[11] Bartholdi, J., Tovey, C.A., and Trick, M.A., 1989, “Voting schemes for which it can be difficult to tell who won the election,” Social Choice and Welfare, Vol.6, No.2, pp. 57–165.
[12] Gonzalez-Pachon, J. and Romero, C., 2001, “Aggregation of partial ordinal rankings: An interval goal programming approach,” Computers and Operations Research, Vol.28, No.8, pp. 827–834.
[13] Hochbaum, D.S. and Levin, A., 2006, “Methodologies and algorithms for group-rankings decision,” Management Science, Vol.52, No.9, pp. 1394–1408.
[14] Bogart, K., 1973, “Preference structures I: Distances between transitive preference relations,” Journal of Math Sociology, Vol.3, pp. 49–67.
[15] Bogart, K., 1975, “Preference structures II: Distances between asymmetric relations,” SIAM Journal of Applied Mathematics, Vol.29, No.2, pp. 254–265.
[16] Cook, W.D., Kress, M., and Seiford, L., 1986, “An axiomatic approach to distance on partial orders,” Revue Automatique, Informatique et Recherche Operationnelle, Vol.20, No.2, pp. 115–122.
[17] Cook, W.D., Kress, M., and Seiford, L., 1986 “Information and preference in partial orders: A bimatrix representation,” Psychometrika, Vol.51, No.2, pp. 197–207.
[18] Cook, W.D., Kress, M., and Seiford, L., 1996, “A general framework for distance-based consensus in ordinal ranking models,” European Journal of Operational Research, Vol.96, pp. 392–397.
[19] Lewis, H.S. and Butler, T.W., 1993, “An interactive framework for multi-person, multiobjective decisions,” Decision Sciences, Vol.24, No.1, pp. 1-22.
[20] Damart, S., Dias, L.C., and Mousseau, V., 2007, “Supporting groups in sorting decisions: methodology and use of a multi-criteria aggregation/disaggregation DSS,” Decision Support Systems, Vol.43, No,4, pp. 1464–1475.
[21] Chen, Y. L. and Cheng, L.C., 2009, “Mining maximum consensus sequences from group ranking data,” European Journal of Operational Research, Vol.198, pp. 241–251.
[22] Han, J., and Kamber, M., 2006, Data mining concepts and techniques, Morgan Kaufmann.
[23] Ester, M., Kriegel, H.P., and Xu, X., 1995, “Knowledge discovery in large spatial databases: Focusing techniques for efficient class identification,” Lecture Notes in Computer Science, Vol.951, pp.67-82.
[24] Yan, H., Chen, K., Liu, L., and Bae, J., 2009, “Determining the best K for clustering transactional datasets: A coverage density-based approach,” Data & Knowledge Engineering, Vol.68, No.1, pp.1-30.
[25] Huang, Z., 1988, “Extensions to the K-modes algorithm for clustering large data sets with categorical values,” Data Mining and Knowledge Discovery, Vol.2, No.3, pp. 283-304.
[26] Couto, J., 2005, “Kernel K-Means for Categorical Data,” Lecture Notes in Computer Science, Vol.3646, pp. 46-56.
[27] Ahmad, A. and Dey, L., 2007, “A k-mean clustering algorithm for mixed numeric and categorical data,” Data & Knowledge Engineering, Vol.63, pp. 503–527.
[28] Chen, C.W., Luo, J., and Parker, K. J., 1998, “Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications,” IEEE Transactions on Image Processing, Vol.7, No.12, pp. 1673-1683.
[29] Feng, Q., 2004, “Restoration of Single-Channel Currents Using the Segmental k-Means Method Based on Hidden Markov Modeling,” Biophysical Journal, Vol.86, pp. 1488-1501.

指導教授

陳彥良(Yen-liang Chen)

審核日期

2011-7-12

推文