English  |  正體中文  |  简体中文  |  Items with full text/Total items : 88295/117812 (75%)
Visitors : 23399363      Online Users : 57
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/118219
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/118219


    Title: 混和型資料之距離矩陣計算
    Calculation of Dissimilarity Matrix for Mixed-type Data
    Authors: 姜順貿
    Jiang, Shun-Mao
    Contributors: 周珮婷
    姜順貿
    Jiang, Shun-Mao
    Keywords: 混和型資料
    分群
    距離矩陣
    距離學習
    Mixed-type data
    Clustering
    Distance matrix
    Distance learning
    Date: 2018
    Issue Date: 2018-07-03 17:23:30 (UTC+8)
    Abstract: 資料分群是資料探勘常見的一種方式,而分群需使用資料間距離的資訊,如何定義資料間距離成為一大挑戰。在資料收集越來越便利的情況下,資料通常為混和型資料,這會產生兩類型的問題,分別為類別距離的計算與結合連續與類別變數的方式。本篇方法透過區分其它相關變數的能力來定義類別距離,如未有相關變數則只考慮變數自己本身。另一方面,連續變數經過離散化計算對應權重後,再對原始連續變數做正規化轉換,以歐式距離的計算方式乘上權重,最後與類別距離做結合得到最終總距離。
    為驗證本篇方法的合理性,使用階層式分群,透過一些真實的資料與過去文獻的方法做比較,結果顯示提出的方法具有可比性(comparable),且整體平均表現最佳,可應用在各種類型資料上。此外對本篇方法求得的距離矩陣,做熱圖視覺化可以發現在大部份資料上,仍保有原始類別數等特質或從資料變數上,找到另一種相近的詮釋。
    Clustering is a common method for data mining. It requires the information about the distance between observations. The way to define the distance becomes a big challenge due to the convenience of data collection. Datasets are in more complex structures, such as mixed-type. Two types of problems have arisen: how to measure the distances between categorical variables and how to measure the distances for mixed variables. The current study proposed an algorithm to define the distance of categorical variables by the ability of distinguishing other related variables. On the other hand, for continuous variables, first, variables were normalized and weighted Euclidean distances were calculated. Then, two distances we calculated above were combined to find a final distance.
    Hierarchical clustering was used to verify the performance of proposed method, through some real-world data compared with the methods of the previous paper. The experiments results showed that the proposed method was comparable with other methods. The overall average performance was the best. The technique can be applied to all types of the data. In addition, by visualizing the proposed distance matrix from the heat maps, we found that the number of cluster patterns were the same as the level of class in the majority of our examples.
    Reference: 1.A. Ahmad, L. Dey, “A k-mean clustering algorithm for mixed numeric and categorical data” , Data & Knowledge Engineering vol. 63, November 2007, pp.503-527.
    2.C. Stanfill, D. Waltz, “Toward memory-based reasoning” , Commun. ACM 29(12), 1986, pp. 1213-1228.
    3.D.R. Wilson, T.R. Martinez, “Improved heterogeneous distance functions” , J. Artif. Intell. Res. 6, 1997, pp. 1-34.
    4.D. Ienco, R. G. Pensa, and R. Meo, “From context to distance: Learning dissimilarity for categorical data clustering” , ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 6, no. 1, March 2012.
    5.J. C. Gower and P. Legendre, “Metric and Euclidean properties of dissimilarity coefficients” , J. Classification, vol. 3, no. 1, 1986, pp. 5–48.
    6.L. Yu & H. LIU, “Feature selection for high-dimensional data: A fast correlation-based filter solution” . In Proceedings of ICML 2003. Washington, DC, USA, 2003, pp. 856–863.
    7.L. Hubert, P. Arabie, “Comparing partitions” , Journal of Classification, vol 2, issue 1, December 1985, pp. 193-218.
    8.M. Ring, F. Otto, M. Becker, T. Niebler, D. Landes, A. Hotho, “ConDist: A Context-Driven Categorical Distance Measure” , Machine Leraing and Knowledge Discovery in Databases, 2015 , pp. 251-266.
    9.S. Boriah, V. Chandola, V. Kumar, “Similarity measures for categorical data: A comparative evaluation” , In: Proc. SIAM Int. Conference on Data Mining, 2008 , pp. 243–254.
    10.S. C. Johnson, “Hierarchical clustering schemes” , Psychometrika, vol 32, no 3, September, 1967
    11.V. Batagelj and M. Bren, “Comparing resemblance measures,” J. Classification, vol. 12, no. 1, 1995, pp. 73–90.
    12.Z. Huang, “Extensions to the k-means algorithm for clustering large data sets with categorical values,” Data Mining and Knowledge Discovery, vol. 2, no. 3, 1998, pp. 283–304.
    13.Z. Hubálek, “Coefficients of association and similarity, based on binary (presence–absence) data: An evaluation,” Biol. Rev., vol. 57, no. 4, 1982, pp. 669–689.
    Description: 碩士
    國立政治大學
    統計學系
    105354016
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105354016
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.STAT.002.2018.B03
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    401601.pdf2398KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback