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研究生:黃昭頴
研究生(外文):Chao-Ying Huang
論文名稱:以頻繁樣式樹資料探勘技術評估使用者身心健康指標之研究
論文名稱(外文):Applying Frequent-Pattern Trees in Data Mining to Evaluate Users'' Physical Fitness and Mental Health Conditions
指導教授:黃有評黃有評引用關係張文中
指導教授(外文):Yo-Ping HuangWen-Chung Chang
口試委員:劉珣瑛姚立德
口試日期:2012-07-11
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:127
中文關鍵詞:憂鬱症身心健康評估資料探勘關聯法則
外文關鍵詞:DepressionPhysical and Mental Health EvaluationData MiningAssociation Rules
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由於生活壓力的增加和步調的緊湊,憂鬱症人口不斷向上攀升,憂鬱症的診斷與治療已是不容忽視的一環。憂鬱症的嚴重程度是作為精神科門診前的一項重要參考因素,傳統的門診服務前是採用紙本問卷作答方式讓患者填寫,再由專業助理進行後續的統計分析,因此無法立即判斷憂鬱症程度。本論文設計一身心健康評估診斷系統,包括憂鬱症、焦慮症、睡眠品質、兒童活動量分析和酒癮篩檢。患者在門診前可直接在系統上作答,填寫完後可立即獲得評估結果。患者作答內容可上傳至雲端資料庫儲存,本論文採用頻繁樣式樹以及頻繁樣式增長演算法探勘出患者作答結果間的所有常見項目集,依照關聯法則找出符合的常見項目集。患者的作答結果可立即與資料探勘結果進行比對,提供精神科醫生作出更準確的診斷。針對25,534筆憂鬱症患者的實際作答資料,關聯法則之分數級距在3~5、最小支持度在10~30%及最低信心水準在50~80%時,模擬結果顯示,本研究所採用的資料探勘技術可以探勘出常見項目間以及患者作答間之關聯法則。此外,針對分別由亂數產生作答資料的焦慮症、睡眠品質以及酒癮篩檢,模擬之探勘結果亦顯示可提供醫師判斷精神疾病程度之參考。

Due to the pressure from daily life, there is an increase in depression population. The diagnosis and treatment of depression is indispensible for patients. The severity of depression is an important reference factor before psychiatric outpatient service. But the traditional questionnaire is answered by pen on the papers. Then, the responded questionnaire is analyzed by professional assistants. In this way, we could not determine the depression degree immediately. This study is aimed on designing a physical fitness and mental health evaluation system that includes depression, anxiety disorder, sleep quality, children''s physical activity and alcohol screening. Patients can answer questionnaire and browse the results directly on the system before outpatient service. The responded questionnaire and analyzed results are automatically uploaded and saved to the cloud database. This study applies frequent pattern trees (FP-tree) and frequent pattern growth (FP-growth) algorithms to discover all the common associations among the records. A new patient''s responded questionnaire can be compared with the data mining results that can provide psychiatrists with valuable information to make more accurate diagnosis. This research analyzed the association rules from combinations of various grade intervals of 3~5, minimum supports of 10~30% and minimum confidence of 50~80% on the actual 25,534 records in our database. The result shows that the proposed system can find frequent itemsets and interesting association rules from databases. In addition, the simulation results from randomly generated data of anxiety disorder, sleep quality, and alcohol screening are also valuable references for psychiatrists to diagnose psychiatry.

摘 要 i
ABSTRACT ii
誌 謝 iv
目 錄 v
表目錄 vii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 文獻探討 4
1.4 研究方法 4
1.5 論文架構 5
第二章 相關技術及運用探討 6
2.1 雲端運算(Cloud Computing) 6
2.1.1 雲端基本特徵(Cloud Essential Characteristics) 8
2.1.2 雲端佈署模型(Cloud Deployment Models) 9
2.1.3 雲端服務模式(Cloud Service Models) 11
2.2 資料探勘(Data Mining) 13
2.2.1 關聯分析(Association Analysis) 17
2.2.2 頻繁樣式樹演算法(Frequent-Pattern Tree) 19
2.2.3 頻繁樣式增長演算法(Frequent-Pattern Growth) 21
2.2.4 群集分析(Clustering) 25
2.2.5 時間資料探勘(Temporal Data Mining) 28
2.3 程式語言Flash ActionScript 3.0 32
2.3.1 事件監聽器(Event Listener) 32
2.3.2 影片剪輯(MovieClip) 34
2.3.3 載入外部圖像、SWF(Shockwave Flash)檔 36
2.3.4 播放外部FLV 37
2.3.5 網域安全 37
2.3.6 Socket通訊連線 40
2.3.7 發佈設定AIR(Adobe Integrated Runtime)檔 42
第三章 系統架構與設計 45
3.1 系統架構 45
3.1.1 硬體架構 45
3.1.2 軟體架構 49
3.2 系統流程 52
3.2.1 醫療教育宣導系統流程 52
3.2.2 身心健康評估診斷系統流程 54
3.2.3 醫療人員查詢系統流程 54
3.2.4 資料探勘系統流程 57
3.3 身心健康評估量表設計 59
3.3.1 國際憂鬱症量表 59
3.3.2 貝克焦慮量表(BAI) 61
3.3.3 匹茲堡睡眠品質量表(PSQI) 63
3.3.4 兒童活動量分析量表(SNAP-IV) 67
3.3.5 酒癮篩檢量表 69
3.4 開發環境 72
3.4.1 硬體 72
3.4.2 軟體 73
第四章 實驗結果與分析 74
4.1 系統介面 74
4.1.1 醫療教育宣導系統介面 76
4.1.2 身心健康評估診斷系統介面 80
4.1.3 醫療人員查詢與資料探勘系統介面 86
4.2 資料探勘實驗結果 89
4.2.1 程式執行驗證 90
4.2.2 資料探勘結果 93
4.2.3 使用者作答結果與資料探勘結果之比對 112
第五章 結論與未來展望 120
5.1 結論 120
5.2 未來展望 121
參考文獻 122

[1] 臺灣憂鬱症防治協會,http://www.depression.org.tw/index.asp
[2] 行政院衛生署 國民健康局,http://www.bhp.doh.gov.tw/BHPnet/Portal/
[3] 自殺防治中心,http://www.tspc.doh.gov.tw/tspc/portal/index/index.jsp
[4] J.-S. Yoo, “Similarity-Profiled temporal association mining,” IEEE Trans. on Knowledge and Data Engineering, vol. 21, no. 8, pp.1147-1161, August 2009.
[5] D. Perera, J. Kay, I. W. Koprinska and K. Yacef, “Clustering and sequential pattern mining of online collaborative learning data,” IEEE Trans. on Knowledge and Data Engineering, vol. 21, no. 6, pp.759-772, June 2009.
[6] 維基百科 – 雲端運算,
http://zh.wikipedia.org/zh-tw/%E9%9B%B2%E7%AB%AF%E9%81%8B%E7%AE%97#cite_note-0。
[7] I. Bojanova and A. Samba, “Analysis of cloud computing delivery architecture models,” in Proc. of IEEE Int. Conf. on Advanced Information Networking and Applications, Biopolis, Singapore, pp.453-458, March 2011.
[8] NIST SP 800-145, The NIST Definition of Cloud Computing.
[9] 國土資訊系統通訊季刊 第74期,格網與雲端技術與GIS。
[10] 陳瀅,雲端策略:雲端運算與虛擬化技術,天下雜誌,臺北市,2010年。
[11] D. Olson and Y. Shi,資料探勘,高立圖書有限公司,臺北市,2008年。
[12] R. J. Roiger and M. W. Geatz,資料探勘,東華書局,臺北市,2004年。
[13] 鄧政宏,基於視覺式多點觸控系統之使用者觸控模式分析,碩士論文,國立臺北科技大學電機工程研究所,臺北,2011。
[14] 林家平,以資料探勘為基礎之貨架管理決策模式,碩士論文,國立臺北科技大學商業自動化與管理研究所,臺北,2003。
[15] 邱愛倫,資料探勘分析顧客行為改變,碩士論文,國立臺北科技大學商業自動化與管理研究所,臺北,2003。
[16] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Third Edition. San Francisco: Morgan Kaufmann, U.S.A., 2011.
[17] R. Agrawal, T. Imielinski and A. Swami, “Mining association rules between sets of items in large database,” in Proc. of ACM SIGMOD Int. Conf. on Management of Data, Washington D.C., U.S.A., pp.207-216, May 1993.
[18] C. Berberidis, L. Angelis and I. Vlahavas, “Inter-transaction association rules mining for rare events prediction,” in Proc. of the 3rd Int. Conf. on Artificial Intelligence, Samos, Greece, pp.308-317, May 2004.
[19] E. Georgii, L. Richter, U. Ruckert and S. Kramer, “Analyzing microarray data using quantitative association rules,” IEEE Trans. on Bioinformatics, vol. 21, supplement 2, pp.ii123-ii129, September 2005.
[20] B. Berendt, “The semantics of frequent subgraphs: Mining and navigation pattern analysis,” in Proc. of Learning, Knowledge Discovery, and Adaptivity Workshop on Knowledge Discovery, Saarbrücken, Germany, pp.91-102, October 2005.
[21] K. Wang, Y. He, D. Cheung and F. Chin, “Mining confident rules without support requirement,” in Proc. of Int. Conf. on Information and Knowledge, Atlanta, GA, U.S.A., pp.89-96, November 2001.
[22] S. Sun and J. Zambreno, “Design and analysis of a reconfigurable platform for frequent pattern mining,” IEEE Trans. on Parallel and Distributed Systems, vol. 22, no. 9, pp.1497-1505, September 2011.
[23] Y. Huang and L. Zhang, “A framework for mining sequential patterns from spatio-temporal event data sets,” IEEE Trans. on Knowledge Analysis and Data Engineering, vol. 20, no. 4, pp.433-448, April 2008.
[24] S. X. Fan, J. S. Yeh and Y. L. Lin, “Hybrid temporal pattern mining with time grain on stock index,” in Proc. of Int. Conf. on Genetic and Evolutionary Computing, Xiamen, China, pp.212-215, September 2011.
[25] T. Guns, S. Nijssen, A. Zimmermann and L. D. Raedt, “Declarative heuristic search for pattern set mining,” in Proc. of Int. Conf. on Data Mining Workshops, Vancouver, British Columbia, Canada, pp.1104-1111, December 2011.
[26] J.-W. Huang and C.-Y. Tseng, “A general model for sequential pattern mining with a progressive database,” IEEE Trans. on Knowledge Analysis and Data Engineering, vol. 20, no. 9, pp.1153-1167, September 2008.
[27] A. Appice, M. Ceci, A. Lanza, F.A. Lisi and D. Malerba, “Discovery of spatial association rules in geo-referenced census data: A relational mining approach,” Intelligent Data Analysis, vol. 7, no. 6, pp.541-566, December 2003.
[28] Q. Ding and W. Perrizo, “PARM- An efficient algorithm to mine association rules from spatial data,” IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 38, no. 6, pp.1513-1524, December 2008.
[29] Y. Huang, S. Shekhar and H. Xiong, “Discovering co-location patterns from spatial datasets: a general approach,” IEEE Trans. on Knowledge and Data Engineering, vol. 16, no. 12, pp.1472-1485, December 2004.
[30] D.-A. Chiang and C.-T. Wang, “The cyclic model analysis on sequential patterns,” IEEE Trans. on Knowledge Analysis and Data Engineering, vol. 21, no. 11, pp.1617-1628, November. 2009.
[31] R.T. Ng and J. Han, “CLARANS: a method for clustering objects for spatial data mining,” IEEE Trans. on Knowledge and Data Engineering, vol. 14, no. 5, pp.1003-1016, September 2002.
[32] X. Shang, K. Sattler and I. Geist, “SQL based frequent pattern mining without candidate generation,” in Proc. of the 2004 ACM Symp. Conf. on Applied Computing, Nicosia, Cyprus, pp.618-619, March 2004.
[33] C.-M. Cha and Y.-C. Tai, “An SQL-based improvement of the FP-tree construction technique,” Information Management Research, vol. 6, pp.31-46, July 2006.
[34] 陳彥良、趙書榮、陳禹辰,“幾個快速挖掘關聯規則的資料探勘方法”,電子商務學報第5卷第2期,民國92年6月。
[35] J. Roberto and Jr. Bayardo, “Efficiently mining long patterns from databases,” in Proc. of ACM SIGMOD Int. Conf. on Management of Data, Seattle, WA, U.S.A., pp.85-93, June 1998.
[36] N. Pasquier, Y. Bastide, R. Taouil and L. Lakhal, “Efficient mining of association rules using closed itemset lattices,” Information Systems, vol. 24, no. 1, pp.25-46, March 1999.
[37] J. Han, J. Pei and Y. Yin, “Mining frequent patterns without candidate generation,” in Proc. of ACM SIGMOD Int. Conf. on Management of Data, Dallas, TX, U.S.A., pp.1-12, May 2000.
[38] L. J. Zhang, S. Cheng, C. K. Chang and Q. Zhou, “A pattern-recognition-based algorithm and case study for clustering and selecting business services,” IEEE Trans. on Systems, Man and Cybernetics, vol. 42, no. 1, pp.102-114, January 2012.
[39] J. Li, B. Shao, T. Li and M. Ogihara, “Hierarchical co-clustering: a new way to organize the music data,” IEEE Trans. on Multimedia, vol. 14, no. 2, pp.471-481, April 2012.
[40] K. Tasdemir, P. Milenov and B. Tapsall, “Topology-based hierarchical clustering of self-organizing maps,” IEEE Trans. on Neural Networks, vol. 22, no. 3, pp.474-485, March 2011.
[41] J. Weaver, K. Mock and B. Hoanca, “Gaze-based password authentication through automatic clustering of gaze points,” in Proc. of Int. Conf. on on Systems, Man, and Cybernetics, Anchorage, A.K., U.S.A., pp.2749-2754, October 2011.
[42] A. Omrani, K. Santhisree and Dr. Damodaram, “Clustering sequential data with OPTICS,” in Proc. of IEEE 3rd Int. Conf. on Communication Software and Networks, Xi''an, China, pp.591-594, May 2011.
[43] X. Zhao and Y. Fang. “A Grid-based Spatial Association Mining Method,” in Proc. of IEEE Int. Conf. on Grid and Cooperative Computing, Los Alamitos, California, pp.600-607, August 2007.
[44] X. Li, J. Lu, H. Shi and S. Ma, “An approach for treatment of the incomplete data based on wavecluster and weighted 1-nearest neighbor,” in Proc. of Int. Conf. on Computer Science and Information Technology, Singapore, pp.3-8, April 2009.
[45] J.F. Roddick and M. Spiliopoulou, “A survey of temporal knowledge discovery paradigms and methods,” IEEE Trans. on Knowledge and Data Engineer, vol. 14, no. 4, pp.750-767, August 2002.
[46] J. Han and M. Kamber, Data mining: concepts and techniques, New York: Morgan Kaufmann Publishers, 2001.
[47] R. Agrawal and R.Srikant, “Mining sequential patterns,” in Proc. of Int. Conf. on Data Engineering, Taipei, Taiwan, pp.3-14, March 1995.
[48] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal and M.-C. Hsu, “Freespan: Frequent pattern-projected sequential pattern mining,” in Proc. of Int. Conf. on Knowledge Discovery and Data mining, Boston, MA, U.S.A., pp.355-359, August 2000.
[49] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M.-C. Hsu, “PrefixSpan: Mining sequential patterns efficiently by prefix projected pattern growth,” in Proc. of Int. Conf. on Data Engineering, Heidelberg, Germany, pp.215-224, March 2001.
[50] 孫穎,Flash ActionScript 3殿堂之路,臺北:文魁資訊股份有限公司,2010年。
[51] 田中康博、林拓也,ActionScript 3.0活用範例大辭典,臺北:博碩文化股份有限公司,2009年。
[52] ActionScript 3.0語言和組件參考 - LocalConnection類別,
http://bbs.flash2u.com.tw/_html/demo/Adobe_Flash_CS4_Help/flash/net/LocalConnection.html。
[53] 通訊端連線,
http://help.adobe.com/zh_TW/ActionScript/3.0_ProgrammingAS3/WS5b3ccc516d4fbf351e63e3d118a9b90204-7cfb.html。
[54] ActionScript 3.0語言和組件參考 - Socket類別,
http://bbs.flash2u.com.tw/_html/demo/Adobe_Flash_CS4_Help/flash/net/Socket.html。
[55] Creating a socket server in Adobe AIR 2,
http://www.adobe.com/devnet/air/flex/articles/creating_socket_server.html。
[56] Adobe ActionScript 3.0安全執行程序,
http://help.adobe.com/zh_TW/ActionScript/3.0_ProgrammingAS3/WS5b3ccc516d4fbf351e63e3d118a9b90204-7e3f.html#WS5b3ccc516d4fbf351e63e3d118a9b90204-7c52。

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