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題名：Extended Self-Organizing Map on Transactional Data
書刊名：資訊管理學報
作者：廖文忠／許中川
作者(外文)：Liao, Wen-chung／Hsu, Chung-chian
出版日期：2012
卷期：19:1
頁次：頁185-216
主題關鍵詞：交易型資料；自組映射圖；概念階層；交易型資料距離函數；概念樹；Transactional data；Self-organizing map；Concept hierarchy；Distance function on transactions；Concept tree
原始連結：連回原系統網址new window
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在許多應用領域，交易紀錄反映個人行為上的偏好或習慣，若將交易紀錄適 當分群，即可將不同行為類型的個人分到不同群組。交易型資料通常有概念階層 伴隨，概念階層反映所有可能交易項目之間的相關性，然而，概念階層卻被大多 數的分群演算法忽略，因此，易將相似度高的交易資料分屬不同群組；除此，分 群結果通常不易被使用者觀看。本論文目的在延伸自組映射圖探勘具概念階層的 交易資料，我們稱之為SetSOM；SetSOM 可將交易資料映射至二維平面上，同時 保有交易資料在其資料空間上的拓樸關係且可被觀看。利用人造資料及實際蒐集 的交易型資料，進行實驗發現，SetSOM 無論在執行時間、視覺觀看品質、映射品 質、及分群品質均高於其他自組映射圖的表現，包括SCM 及SOM。
In many application domains, transactions are the records of personal activities. Transactions always reveal personal behavior customs, so clustering the transactional data can divide individuals into different segments. Transactional data are often accompanied with a concept hierarchy, which defines the relevancy among all of the possible items in transactional data. However, most of clustering methods in transactional data ignore the existing of the concept hierarchy. Owing to the lack of the relevancy provided by the concept hierarchy, clustering algorithms tend to separate some similar patterns into different clusters. Besides, their clustering results are not easy to be viewed by users. The purpose of this study is to propose an extended SOM model which can handle transactional data accompanied with a concept hierarchy. The new SOM model is named as SetSOM. It can project the transactional data into a two-dimensional map; in the meanwhile, the topological order of the transactional data can be preserved and visualized in the 2-D map. Experiments on synthetic and real datasets were conducted, and the results demonstrated the SetSOM outperforms other SOM models in execution time, and the qualities of visualization, mapping and clustering.
期刊論文
1.Chen, D. R.、Chang, R. F.、Huang, Y. L.(2000)。Breast cancer diagnosis using self-organizing map for sonography。Ultrasound in Medicine and Biology，1(26)，405-411。  new window
2.Vathy-Fogarassy, A.、Abonyi, J.(2009)。Local and global mappings of topology representing networks。Information Sciences，179(21)，3791-3803。  new window
3.Hsu, C. C.(2006)。Generalizing self-organizing map for categorical data。IEEE Transactions on Neural Networks，17(2)，294-304。  new window
4.Günter, S.、Bunke, H.(2002)。Self-organizing map for clustering in the graph domain。Pattern Recognition Letters，23(4)，405-417。  new window
5.Yeh, M. F.、Chang, K. C.(2006)。A self-organizing CMAC network with gray credit assignment。IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics，36(3)，623-635。  new window
6.Kohonen, T.(1982)。Self-organized formation of topologically correct feature maps。Biological cybernetics，43(1)，59-69。  new window
7.Kohonen, T.、Kaski, S.、Lagus, K.、Salojavi, J.、Honkela, J.、Paatero, V.、Saarela, A.(2000)。Self-Organization of a Massive Document Collection。IEEE Transactions on Neural Networks，11(3)，574-585。  new window
8.He, Z.、Xu, X.、Deng, S.(2005)。TCSOM: clustering transactions using self-organizing map。Neural Processing Letters，22，249-262。  new window
9.Hua Yan, H.、Chen, K.、Liu, L.、Bae, J.(2009)。Determining the best K for clustering transactional datasets: A coverage density-based approach。Data & Knowledge Engineering，68，28-48。  new window
10.Kaski, S.、Nikkila, J.、Oja, M.、Venna, J.、Toronen, J.、Castren, E.(2003)。Trustworthiness and metrics in visualizing similarity of gene expression。BMC Bioinformatics，4(48)。  new window
11.Kohonen, T.、Somervuo, P.(1998)。Self-organizing maps on symbol strings。Neurocomputing，21，19-30。  new window
12.Kohonen, T.、Somervuo, P.(2002)。How to make large self-organizing maps for nonvectorial data。Neural Networks，15，945-952。  new window
13.Lampinen, J.、Oja, E.(1992)。Clustering properties of hierarchical self-organizing maps。Journal of Mathematical Imaging and Vision，2，261-272。  new window
14.Somervuo, P.(2004)。Online algorithm for the self-organizing map of symbol strings'。Neural Networks，17，1231-1239。  new window
會議論文
1.Ultsch, A.(2003)。Maps for the visualization of high-dimensional data spaces。The 4th Workshop on Self-Organizing Maps。Kitakyushu。225-230。  new window
2.Guha, S.、Rastogi, R.、Shim, K.(1999)。ROCK: A Robust Clustering Algorithm for Categorical Attributes。15th International Conference on Data Engineering，512-521。  new window
3.Flanagan, J.A.(2003)。Unsupervised clustering of symbol strings。  new window
4.Hammer, B.、Micheli, A.、Neubauer, N.、Sperduti, A.、Strickert, M.(2005)。Self-Organizing Maps for Time Series。  new window
5.Himberg, J.、Flanagan, J.A.、Mantyjarvi, J.(2003)。Towards context awareness using Symbol Clustering Map。  new window
6.Hsu, C.C.、Wang, K.M.、Wang, S.H.(2006)。GViSOM for multivariate mixed data projection and structure visualization3300-3305。  new window
7.Kohonen, T.、Oja, O.E.、Simula, A. Visa, S.A.、Kangas, J.(1996)。Engineering applications of the self-organizing map84(10)，1358-1384。  new window
8.Venna, J.、Kaski, S.(2005)。Local multidimensional scaling with controlled tradeoff between trustworthiness and continuity695-702。  new window
9.Wang, K.、Xu, C.、Liu, B.(1999)。Clustering transactions using large items483-490。  new window
10.Yang, Y.、Guan, S.、You, J.(2002)。CLOPE: a fast and effective clustering algorithm for transactional data682-687。  new window
11.Zhang, B.、Xiang, Q.、Lu, H.、Shen, J.、Wang, Y.(2009)。Comprehensive query-dependent fusion using regression-on-folksonomies: a case study of multimodal music search。  new window
研究報告
1.Kohonen, T.(1996)。Self-organizing maps of symbol strings。Laboratory of Computer and Information Science, Helsinki University of Technology。  new window
圖書
1.Kohonen, T.(2001)。Self-organizing maps。New York：Springer。  new window
2.Kohonen, T.(1996)。SOM_PAK: The Self-Organizing Map Program Package。Espoo, Finland：Helsinki University of Technology, Laboratory of Computer and Information Science。  new window
3.Han, Jiawei、Kamber, Micheline(2000)。Data mining: Concepts and techniques。Morgan Kaufmann Publishers。  new window
其他
1.Vesanto, J.，Himberg, J.，Alhoniemi, E.，Parhankangas, J.(2000)。SOM Toolbox for Matlab 5。  new window
 
 
 
 
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