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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/110780


    Title: 應用集群分析於智慧型手機使用目的之探討
    Clustering analysis for smartphone usage
    Authors: 蔡儀君
    Tsai, Yi-Chun
    Contributors: 翁久幸
    Weng, Chiu-Hsing
    蔡儀君
    Tsai, Yi-Chun
    Keywords: 集群分析
    因素分析
    分類
    Cluster analysis
    Factor analysis
    Classify
    Date: 2017
    Issue Date: 2017-07-11 11:25:25 (UTC+8)
    Abstract: 在這科技飛騰的時代，智慧型手機使用日益普及，根據eMarketer於2016年公布台灣使用智慧型手機人口佔總人口73.4%，相較於新加坡71.8%與南韓70.4%的使用率，此比率高居全球之冠，各行業該如何運用智慧型手機市場為自己駐足的行業開創佳績，成為現今人們廣為關注的話題。
    本論文研究所用之資料取自「科技部傳播調查資料庫第一期第三次（2014）：媒體的娛樂與社交功能」一般民眾（18 歲以上）之問卷資料。首先對樣本基本資料結構與特性進行描述，接著將智慧型手機使用的相關題項找出，並進行因素分析找出因素構面作為分群變數，藉由兩階段分群法進行分群，探討其各群間相關之特性與智慧型手機使用之目的。爾後從性別、年齡與教育程度等基本人口變項進行分析，進一步了解不同人口基本結構智慧型手機之使用目的之差異情形，並將「網路素養」、「社交媒體」等相關題組進行因素分析，萃取出重要共同因素後並予以命名，以探討不同媒體社交功能使用情形與智慧型手機使用目的之相關性，最後將人口基本結構與共同因素視為變數，分別採用CART、C5.0、QUEST與CHAID四種決策樹分析方法對「集群一」、「集群二」智慧型手機高度使用者進行模型之建構，使各行業可針對欲探討之集群提出行銷方針。
    With the rapid development of technology, the Internet and mobile phones play an important role in our lives. According to eMarketer 2016, 73.4% of Taiwan`s population use smartphones, compared to 71.8% in Singapore and 70.4% in South Korea , Taiwan tops the list of the world. How to create success by using smartphone market is an important issue today.
    The data used in this thesis was taken from the Ministry of Science and Technology Survey in 2014. The survey topic was media entertainment and social functions, based on general public who are 18 years old or older. First, the structures of the sample are described. Next, we extract factors by using factor analysis. The factors are used as the cluster variables. This study uses two-stage method to cluster and explore characteristics of the relevant groups for the smartphone usage. Then, we analyze demographic variables to understand different populations of smart phones usage, and extract common factors of "Internet Literacy" and "Social Media" by using factor analysis. Finally, the basic structure of the population and the common factors are used to classify smartphone users, which helps to provide marketing guidelines.
    Reference: 一、中文文獻
    1.謝邦昌（1998）。「統計教室-多變量分析(二)－因素分析」，中國統計通訊，第9卷，第8期，頁31-41。
    2.魏錫鈴（1999）。行動電話消費者購買行為及其市場區隔之研究--以北部地區居民為例，國立交通大學，新竹。
    3.陳順宇（2005）。多變量分析（四版）。台北市：華泰書局。
    4.黃俊英（2007）。多變量分析（七版）。台北市：翰蘆圖書。
    5.李維蔓、詹岱倫（2009）。SPSS統計分析與專題應用（初版）。台北市：學貫行銷。
    6.薛薇、陳歡歌（2010）。Clementine數據挖掘方法及應用（初版）。大陸：電子工業出版社。
    7.范惟翔（2011）。市場調查與專題研究實務（初版）。新北市：經峯數位。
    8.黃曉翎（2012）。銀行財富管理客戶貢獻分群機制之建立。國立台北科技大學，台北。
    9.曾仁人（2013）。資料採礦在網路消費行為預測模型之應用。國立政治大學，台北。
    10.王筱薇（2014）。不同網路購物涉入程度之消費者行為探討。國立政治大學，台北。
    11.賴思穎（2014）。應用集群分析於商業套餐設計之研究。國立政治大學，台北。
    12.簡明輝（2014）。消費者行為（第三版）。新北市：新文京。
    13.王志軒（2015）。結合科技接受模式與資料採礦方法進行智慧型電視之購買預測。國立交通大學，新竹。
    14.李宗祐（2015）。應用資料採礦於肝膿瘍患者罹患癌症之研究。東海大學。台中。
    15.葉樺蓁（2015）。以Booking.com為依據之旅館住宿滿意度資料採礦。東海大學。台中。


    二、英文文獻
    1.Bartlett(1951), M.S.“A further Note on Tests of Significance in Factor Analysis,＂British Journal of Statistical Psychology, 4(1), pp.1-2
    2.Breiman(1984), L., J. Friedman, R. Olshen, and C. Stone, Classification and regression trees, Wadsworth Books, pp.358
    3.Cattell, Raymond B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), pp.245–276
    4.Chiu, T. - Fang, D.- Chen, J. - Wang, Y. - Jeris, C. (2001), A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Database Environment. Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining , SanFrancisco：CA: ACM. , pp263–268
    5.Daniel T. Larose. (2006), Data Mining Methods And Models(1st ed.), New York: John Wiley & Sons. ,pp.294-304
    6.Demby, E. (1973), “Psychographicsand Form Where It Comes”, Lifestyle and
    Psychographics, W.D.Wells (eds.), Chicago AMA, pp.22.
    7.IBM Corporation .(2011), IBM SPSS Modeler 14.2 Algorithms Guide, pp.323-331.
    8.Integral Solutions Limited. (2007), Clementine 12.0 Algorithms Guide.
    9.Integral Solutions Limited. (2007), Clementine 12.0 Modeling Nodes.
    10.Johnson, R.A. and Wichern, D.W. (2002) Applied Multivariate Statistical Analysis, Fifth Edition. Englewood Cliffs, New Jersey: Prentice Hall.
    11.Kaiser, H.F.(1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, pp.141-151.
    12.Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 20, 2, pp119-127.
    13.Kotler, P. (2003), A Framework for Marketing Management(6th ed.), Prentice Hall, N.J.
    14.Loh, W.-Y. and Shih, Y.-S. (1997). Split selection methods for classification trees, Statistica Sinica 7: pp815–840.
    15.Martinez, J. (2010). Driving results. CRM Magazine. Accessed July 6, 2011, from
    http://www.destinationcrm.com/Articles/Editorial/Magazine-Features/ Driving-Results-68090.aspx
    16.Nicosia, F.M. (1966), Consumer Decision Process:Marketing and Advertising
    Implications, Englewood Cliffs, Prentice-Hall, N.J., pp.13-28.
    17.Quinlan, J. R. (1979), Discovering rules from large collections of examples: A case study, in D. Michie, ed., Expert Systems in the Micro Electronic Age, Edinburgh University Press.
    18.Quinlan, J. R. (1993), C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, California.
    19.Quinlan, J. R. (1998). Data Mining Tools see5 and c5.Academic Press.
    20.Raied Salman.,Vojislav Kecman.,Qi Li.,Robert Strack.,&Erick Test.(2011). Two-Stage Clustering with k-Means Algorithm. Recent Trends in Wireless and Mobile Networks,3(4), pp.110-122.
    21.Reichheld, F. F., Markey, R. G., Jr., & Hopton, C. (2000). The loyalty effect-the relationship between loyalty and profits. European Business Journal, 12(3), pp.134–139.
    22.Reinartz, W., & Kumar, V. (2002). The mismanagement of customer loyalty. Harvard Business Review, 80(7), pp.86–94.
    23.Robinson,J.P.,Shaver,P.R.,&Wrightsman,L.S.(1991).Criteria foe scale selection and evaluation. In J.R.Robinson, P.R. Shaver, & L.S. Wrightsman (edss), Measures of personality and social psychological attitudes. San Diego：,Calif.：Academic Press.
    24.Shannon, C.E and Weaver, W. W. (1949) The Mathematical Theory of Communication. University of Illinois Press, Urbana, IL.
    25.Solomon, M. R. (2001). Consumer Behavior :International Edition, 5th, NJ: Prentice Hall.
    26.Spearman, C. (1904). ‘General intelligence’, objectively determined and measured. American Journal of Psychology, 15, pp.201-293.
    27.V. Kumar and Werner Reinartz (2016), “Creating Enduring Customer Value,” Journal of Marketing, 80 (6), pp.36-68.

    三、網路資源
    1.市調機構emarketer：https://www.emarketer.com/
    2.eMarketer：台灣智慧型手機普及率達73.4% 居全球之首：
    https://kknews.cc/zh-tw/tech/mg58g66.html
    3.遠傳企業網站：http://www.fetnet.net/home/
    4.國家通訊傳播委員會：http://www.ncc.gov.tw/chinese/index.aspx
    5.國家發展委員會：http://www.ndc.gov.tw/cp.aspx?n=55c8164714dfd9e9
    6.科技部傳播調查資料庫：http://www.crctaiwan.nctu.edu.tw/
    7.陳士杰機器學習課程：http://sjchen.im.nuu.edu.tw/MachineLearning/final/CLS_DT.pdf
    Description: 碩士
    國立政治大學
    統計學系
    104354008
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104354008
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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