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論文名稱 Title |
基於統計和決策樹來邁向知識發現之資料分類的有效率演算法 Efficient Algorithms for Data Classification toward Knowledge Discovery based on Statistics and Decision Trees |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
115 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2018-07-30 |
繳交日期 Date of Submission |
2018-07-31 |
關鍵字 Keywords |
週期性探勘、知識發現、資料分類、卡方檢定、決策樹 Periodicity Mining, Knowledge Discovery, Decision Tree, Data Classification, Chi-Square Test |
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統計 Statistics |
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中文摘要 |
知識發現在資料庫中乃是著重於從收集的資料中擷取有用資訊的方法。知識發現方法中的其中一個是資料探勘。資料分類是資料探勘中著名且有用的技術之一,其目的是將收集到的資料分類用以分析準確的預測。此外,決策樹是資料分類的模型之一。事實上,一個好的決策樹的關鍵因素就是決定內部節點的因子。在統計檢定中,卡方檢定是分析類別變數A 是否為類別變數B 的顯著因子的好方法之一。根據我們在醫學主題研究論文中的觀察,我們認為危險因子(即卡方統計的顯著因子)與決策樹中重要的決定因子是密切相關的。因此首先,本論文與台灣高雄長庚泌尿科合作研究了慢性腎臟病是膀胱癌的一個重要危險因子,並且提出了統計方法來確認其關係。在此研究中,知識發現需要幾個前處理的步驟,包含資料選擇、清除不明確的資料和資料強化。此外,所得到的危險因子(即重要因子)可以用做決策樹中的決定因子。所以其次,我們利用影響決策樹效能的顯著因子,提出了一種減少決定因子數量和確認決定因子成長順序的方法。在此研究中,我們以公開的棒球資料庫為例來說明我們的方法。事實上,我們在意的是對相同的決策樹中在建構決策樹前,有無進行前處理步驟(即刪除不顯著因子的過程)的效能。因此,我們做了有進行前處理步驟和沒有進行前處理步驟的效能比較。整體來說,我們提出的方法可以被應用在任何其它具有額外類別值屬性的資料庫。第三,探勘週期性模式的結果也有助於知識發現,所以我們提出了一種基於矩陣和探勘週期圖的時間與位置連接的方法。在此研究中,我們與 Rasheed等人提出的suffix tree方法進行比較。對於這三個研究方向中的每一個研究項目,我們顯示出具有一定程度的高精確度、少處理時間和較少儲存空間之貢獻。因此,在本論文中,我們提出了基於統計和決策樹來邁向知識發現之資料分類的有效率演算法。 |
Abstract |
Knowledge discovery in the database focuses upon methodologies for extracting useful information from collection of data. One of approaches for knowledge discovery is data mining. Data classification is one of famous and useful techniques for data mining that assigns categories to collected data in order to analyze the accurate prediction. Moreover, one of models for data classification is a decision tree. In fact, one of key points of a good decision tree is the kind of deciding factors in the internal nodes. In statistical tests, the chi-square test is one of good ways to analyze whether categorical variable A is the significant factor to categorical variable B. From our observation from research papers in the topic of medicine, we consider that the risk factor (i.e., the significant factor of the chi-square in statistics) is strongly related to the important deciding factor in the decision tree. Therefore, in this dissertation, first, we study the chronic kidney disease as an important risk factor for the bladder cancer by cooperating with Department of Urology, Chang Gung Memorial Hospital, Kaohsiung, Taiwan, and we propose a statistic approach to check the relation. In such a study, we need several preprocessing steps of knowledge discovery, including data selection, cleaning unclear data, and data enrichment. Moreover, the resulting risk factor (i.e., the significant factor) can be used as a deciding factor in a decision tree. Second, we make use of the significant factor to improve the performance of the decision tree, and we propose an approach which aims to reduce the number of deciding factors and decide the order of deciding factors in a decision tree. In such a study, we take the public baseball database as an example to illustrate our method. In fact, what we care about is the comparison of the performance of the same decision tree algorithm with or without using the preprocessing step, i.e., the pruning process of insignificant factors, before we construct the decision tree. Therefore, we compare the performance of the case that it uses the preprocessing step and the case that it does not use the preprocessing step. Overall, our proposed method can be applied to any other database for an extra attribute with a class value. Third, the result of mining periodicity patterns can also be helpful in knowledge discovery, and we propose a time-position join method based on a matrix and a graph for periodicity mining. In such a study, we make a comparison with the suffix tree approach proposed by Rasheed et al. For each of those three directions for research, we have shown that our contribution in terms of high accuracy, short processing time and less storage to some degree. Consequently, in this dissertation, we have proposed efficient algorithms for data classification toward knowledge discovery based on statistics and decision trees. |
目次 Table of Contents |
THESIS VALIDATION LETTER i ACKNOWLEDGEMENTS ii ABSTRACT (CHINESE) iii ABSTRACT (ENGLISH) iv LIST OF FIGURES viii LIST OF TABLES xi 1. Introduction 1 1.1 Data Mining 3 1.2 Classification in Data Mining 4 1.3 Chi-square Test in Statistics 6 1.4 An Application of a Decision Tree: Diagnosis for the Bladder Cancer 6 1.5 Algorithms for Constructing the Decision Tree: ID3 and C4.5 7 1.6 Periodicity Mining in Time Series Databses 8 1.7 Motivations and Contributions 8 1.7.1 Chronic Kidney Disease as an Important Risk Factor for Tumor Recurrences, Progression and Overall Survival in Primary Non-Muscle-Invasive Bladder Cancer 10 1.7.2 Applying the Chi-Square Test to Improve the Performance of the Decision Tree for Classification by Taking Baseball Database as an Example 12 1.7.3 A Time-Position Join Method for Periodicity Mining in Time Series Databases 14 1.8 Organization of the Dissertation 14 2. A Survey of Approaches for Knowledge Discovery 15 2.1 The Chi-Square Test 15 2.2 The Decision Tree 19 2.3 Rasheed et al.’s Algorithm for Periodicity Mining 21 2.4 Bladder Cancer 23 3. Chronic Kidney Disease as an Important Risk Factor for Tumor Recurrences, Progression and Overall Survival in Primary Non-Muscle-Invasive Bladder Cancer 26 3.1 Methods 27 3.2 Result 29 3.3 Discussion 31 3.4 Summary 38 4. Applying the Chi-Square Test to Improve the Performance of the Decision Tree for Classification by Taking Baseball Database as an Example 42 4.1 The Proposed Method 43 4.1.1 The Baseball Database 43 4.1.2 Our Proposed Method 45 4.1.3 The Resulting Decision Tree 49 4.2 Performance 53 4.3 Summary 64 5. A Time-Position Join Method for Periodicity Mining in Time Series Databases 66 5.1 The Time-Position Join Method 67 5.1.1 Notations and Definition 67 5.1.2 The Proposed Time-Position Join Method 69 5.2 Performance 84 5.3 Summary 86 6. Conclusions 88 6.1 Summary 88 6.2 The Future Research Direction 91 BIBLIOGRAPHY 92 |
參考文獻 References |
[1] “Chinese Professional Baseball League, Chinese Professional Baseball League 2017 Official Rules,” http://www.cpbl.com.tw/footer/rule/09, 2017. [2] “Chinese Professional Baseball League, Chinese Professional Baseball League Games,” http://www.cpbl.com.tw/eng/structures, 2017. [3] “Chinese Professional Baseball League, Chinese Professional Baseball League Players.,” http://www.cpbl.com.tw/stats/all.html, 2017. [4] “Major League Baseball, Major League Baseball 2017 Official Rules,” http://mlb.mlb.com/mlb/official info/official rules/official rules.jsp, 2017. [5] “World Baseball Softball Confederation, World Baseball Softball Confederation History,” http://www.wbsc.org/wbsc-history/, 2017. [6] R. Agrawal, T. Imieli´nski, and A. Swami, “Mining association rules between sets of items in large databases,” Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216, 1993. [7] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499, 1994. [8] G. Alberti, F. M. Iaia, E. Arcelli, L. Cavaggioni, and E. Rampinini, “Goal Scoring Patterns in Major European Soccer Leagues,” Sport Sciences for Health, Vol. 9, No. 3, pp. 151–153, Dec. 2013. [9] A. Anastasiadis and T. M. de Reijke, “Best Practice in the Treatment of Nonmuscle Invasive Bladder Cancer.,” Biomedical Signal Processing and Control, Vol. 4, No. 1, pp. 13–32, Feb. 2012. [10] D. W. K. Andrews, “Chi-Square Diagnostic Tests for Econometric Models: Introduction and Applications,” Journal of Econometrics, Vol. 37, No. 1, pp. 135–156, Jan. 1988. [11] A. A. Antunes, L. J. Nesrallah, M. F. DallOglio, C. E. Maluf, C. Camara, K. R. Leite, and M. Srougi, “The Role of Squamous Differentiation in Patients with Transitional Cell Carcinoma of the Bladder Treated with Radical Cystectomy,” International Brazilian Journal of Urology, Vol. 33, No. 3, pp. 339–346, May 2007. [12] W. H. Au, K. C. C. Chan, and X. Yao, “A Novel Evolutionary Data Mining Algorithm with Applications to Churn Prediction,” IEEE Transactions on Evolutionary Computation, Vol. 7, No. 6, pp. 532–545, Dec. 2003. [13] R. Ayyathurai and M. S. Soloway, “Monitoring of the Upper Urinary Tract in Patients with Bladder Cancer,” Indian Journal of Urology, Vol. 27, No. 2, pp. 238–244, April 2011. [14] M. Babjuk, M. Burger, R. Zigeuner, S. F. Shariat, B. W. G. van Rhijn, E. Comperat, R. J. Sylvester, E. Kaasinen, A. Bohle, J. P. Redorta, and M. Roupret, “EAU Guidelines on Non-Muscle-Invasive Urothelial Carcinoma of the Bladder: Update 2013,” European Urology, Vol. 64, No. 4, pp. 639–653, Oct. 2013. [15] C. Berberidis, W. G. Aref, M. Atallah, I. Vlahavas, and A. K. Elmagarmid, “Multiple and Partial Periodicity Mining in Time Series Databases,” Proceedings of the 15th Europe Conference on Artificial Intelligence, pp. 370–374, 2002. [16] M. Bohanec and I. Bratko, “Trading Accuracy for Simplicity in Decision Trees,” Machine Learning, Vol. 15, No. 3, pp. 223–250, June 1994. [17] A. Carmona-Bayonas, P. Jimenez-Fonseca, C. Font, F. Fenoy, R. Otero, C. Beato, J. M. Plasencia, M. Biosca, M. Sanchez, M. Benegas, D. Calvo-Temprano, D. Varona, L. Faez, I. de la Haba, M. Antonio, O. Madridano, M. P. Solis, A. Ramchandani, E. Castanon, P. J. Marchena, M. Martin, F. A. de la Pena, and V. Vicente, “Predicting Serious Complications in Patients with Cancer and Pulmonary Embolism using Decision Tree Modelling: The EPIPHANY Index,” British Journal of Cancer, Vol. 116, pp. 994–1001, April 2017. [18] C. H. Chang, C. M. Yang, and A. H. Yang, “Renal Diagnosis of Chronic Hemodialysis Patients with Urinary Tract Transitional Cell Carcinoma in Taiwan,” Cancer, Vol. 109, No. 8, pp. 1487–1492, April 2007. [19] Y. I. Chang, C. E. Li, R. F. Chen, S. J. Du, and C. Y. Yen, “A Subset-Lattice Algorithm for Mining High Utility Patterns over the Data Stream Sliding Window,” Proceedings of the 2017 National Computer Symposium, 2017. [20] Y. I. Chang, C. E. Li, and T. H. Chen, “A Position-Join Method for Mining Maximum-Length Repeating Patterns in Music Databases,” Proc. of the 2011 National Computer Symposium, pp. 1–12, 2011. [21] Y. I. Chang, C. E. Li, and T. L. Chin, “An Obstruction-Check Approach to Mining Closed Sequential Patterns in Data Streams,” Proceedings of the International Computer Symposium, pp. 516–525, 2014. [22] Y. I. Chang, C. E. Li, T. J. Chou, and C. Y. Yen, “A Weight-Order-Based Lattice Algorithm for Mining Maximal Weighted Frequent Patterns over a Data Stream Sliding Window,” Proceedings of the 4th IEEE International Conference on Applied System Innovation, 2018. [23] Y. I. Chang, C. E. Li, and S. Y. Lin, “The GDense Algorithm for Clustering Data Streams with High Quality,” Proceedings of the 3rd International Conference on Software and Information Engineering, pp. 353–359, 2014. [24] Y. I. Chang, C. E. Li, and W. H. Peng, “An Efficient Subset-Lattice Algorithm for Mining Closed Frequent Itemsets in Data Streams,” Proceedings of the International Conference on Technologies and Applications of Artificial Intelligence, pp. 21–26, 2012. [25] Y. I. Chang, C. E. Li, W. H. Peng, and S. Y. Wang, “Efficient Subset-Lattice Algorithms for Mining Closed Frequent Itemsets and Maximal Frequent Itemsets in Data Streams,” International Journal of Electrical Engineering, Vol. 20, No. 2, pp. 51–63, May 2013. [26] Y. I. Chang, C. E. Li, and Y. S. Tseng, “Vertical-Line-Based Incremental Algorithms for Moving Objects of the Monochromatic and Bichromatic Reverse Nearest Neighbors,” Journal of Software, Vol. 11, No. 5, pp. 512–519, May 2016. [27] Y. I. Chang, C. E. Li, and K. N. Yang, “A KSNA-Tree Algorithm for the Topk Exact Keyword Search in Spatial Databases,” Proc. of the 5th International Conference on Information Technology and Science, pp. 166–170, 2017. [28] Y. I. Chang, M. H. Tsai, C. E. Li, and P. Y. Lin, “A Set-Checking Algorithm for Mining Maximal Frequent Itemset from Data Streams,” Proceedings of the 1st International Conference on Intelligent Technologies and Engineering Systems, pp. 235–241, 2012. [29] Y. I. Chang, M. H. Tsai, C. E. Li, and P. Y. Lin, “A Set-Checking Algorithm for Mining Maximal Frequent Itemset from Data Streams,” Lecture Notes in Electrical Engineering, Vol. 234, pp. 235–241, Feb. 2013. [30] Y. I. Chang, C. C. Wu, J. H. Shen, and C. H. Chen, “Data Classification Based on the Class-Rooted FP-Tree Approach,” Proceedings of IEEE International Conference on Complex, Intelligent and Software Intensive Systems, pp. 506– 511, 2009. [31] T. C. E. Cheng, D. Y. C. Lam, and A. C. L. Yeung, “Adoption of Internet Banking: An Empirical Study in Hong Kong,” Decision Support Systems, Vol. 42, No. 3, pp. 1558–1572, Dec. 2006. [32] R. Conforti, M. de Leoni, M. L. Rosa, W. M. P. van der Aalst, and A. H. M. ter Hofstede, “A Recommendation System for Predicting Risks Across Multiple Business Process Instances,” Decision Support Systems, Vol. 69, pp. 1–19, Jan. 2015. [33] M. G. Elfeky, W. G. Aref, and A. K. Elmagarmid, “Periodicity Detection in Time Series Databases,” IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 7, pp. 875–887, July 2005. [34] E. Frank, M. Hall, L. Trigg, G. Holmes, and I. H. Witten, “Data Mining in Bioinformatics using Weka,” Bioinformatics, Vol. 20, No. 15, pp. 2479–2481, Oct. 2004. [35] A. P. Grollman, “Aristolochic Acid Nephropathy: Harbinger of a Global Iatrogenic Disease,” Environmental and Molecular Mutagenesis, Vol. 54, No. 1, pp. 1–7, Jan. 2013. [36] O. Gulpinar, A. H. Halilioglu, M. I. Gokce, C. Gogus, and S. Baltaci, “The Value of Perioperative Mitomycin C Instillation in Improving Subsequent Bacillus Calmette-Guerin Instillation Efficacy in Intermediate and High-Risk Patients with Non-Muscle Invasive Bladder Cancer: A Prospective Randomized Study,” International Brazilian Journal of Urology, Vol. 38, No. 4, pp. 474–479, July 2012. [37] D. Gusfield, “Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology,” Cambridge University Press, pp. 317–327, 1997. [38] D. J. Hand, “Principles of Data Mining,” Drug Safety, Vol. 30, No. 7, pp. 621– 622, July 2007. [39] E. T. Hebert, E. A. Vandewater, M. S. Businelle, M. B. Harrell, S. H. Kelder, and C. L. Perry, “Feasibility and Reliability of a Mobile Tool to Evaluate Exposure to Tobacco Product Marketing and Messages using Ecological Momentary Assessment,” Addictive Behaviors, Vol. 73, pp. 105–110, Oct. 2017. [40] C. C. Hsu, Y. M. Wang, C. R. Huang, F. J. Sun, J. P. Lin, P. K. Yip, and S. I. Liu, “Sustained Benefit of a Psycho-Educational Training Program for Dementia Caregivers in Taiwan,” International Journal of Gerontology, Vol. 11, No. 1, pp. 31–35, March 2017. [41] K. Y. Huang and C. H. Chang, “SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases,” IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, pp. 774–785, June 2005. [42] Z. Huang, R. Xu, C. Lv, Z. Zhong, L. Zhang, L. Zhu, Y. Tang, and X. Zhao, “A Chronic Obstructive Pulmonary Disease Negatively Influences the Prognosis of Patients with Bladder Urothelial Carcinoma via Hypoxia Inducible Factor- 1 ” International Journal of Clinical and Experimental Medicine, Vol. 7, No. 10, pp. 3344–3353, Oct. 2014. [43] M. C. Jadud and B. Dorn, “Aggregate Compilation Behavior: Findings and Implications from 27,698 Users,” Proceedings of the Eleventh Annual International Conference on International Computing Education Research, pp. 131–139, 2015. [44] F. G. Jesus, M. Rosario, S. Eduardo, U. Miguel, M. P. Luis, O. Antonio, P. Jose, M. Manuel, G. Marcelino, P. Carlos, R. M. Jesus, E. C. Jose, R. Mariano, A. Ander, I. Santiago, M. Pedro, G. Anabel, B. Miguel, and A. M. P. Jose, “The EORTC Tables Overestimate the Risk of Recurrence and Progression in Patients with Non-Muscle-Invasive Bladder Cancer Treated with Bacillus Calmette- Guerin: External Validation of the EORTC Risk Tables,” European Urology, Vol. 60, No. 3, pp. 423–430, Spet. 2011. [45] A. Jimenez, M. C. Monroe, N. Zamora, and J. Benayas, “Trends in Environmental Education for Biodiversity Conservation in Costa Rica,” Environment, Development and Sustainability, Vol. 19, No. 1, pp. 221–238, Feb. 2017. [46] C. H. Kang, C. H. Chen, and P. H. Chiang, “Primary Urothelial Carcinoma of the Upper Urinary Tract in Dialysis Patients with 5-Year Follow-Up,” Japanese Journal of Clinical Oncology, Vol. 40, No. 3, pp. 241–246, March 2010. [47] C. H. Kang, T. J. Yu, H. H. Hsieh, J. W. Yang, K. Shu, C. C. Huang, P. H. Chiang, and Y. L. Shiue, “The Development of Bladder Tumors and Contralateral Upper Urinary Tract Tumors after Primary Transitional Cell Carcinoma of the Upper Urinary Tract,” Cancer, Vol. 98, No. 8, pp. 1620–1626, Oct. 2013. [48] B. L. Kasiske, J. J. Snyder, D. T. Gilbertson, and C. C. Wang, “Cancer after Kidney Transplantation in the United States,” American Journal of Transplantation, Vol. 4, No. 6, pp. 905–913, June 2004. [49] H. Kobayashi, E. Kikuchi, S. Mikami, T. Maeda, N. Tanaka, A. Miyajima, K. Nakagawa, and M. Oya, “Long Term Follow-Up in Patients with Initially Diagnosed Low Grade Ta Non-Muscle Invasive Bladder Tumors: Tumor Recurrence and Worsening Progression,” BMC Urology, Vol. 14, No. 1, pp. 1–5, Jan. 2014. [50] L. A. Kurgan and P. Musilek, “A Survey of Knowledge Discovery and Data Mining Process Models,” The Knowledge Engineering Review, Vol. 21, No. 1, pp. 1–24, March 2006. [51] B. R. Lane, A. K. Smith, B. T. Larson, M. C. Gong, S. C. Campbell, D. Raghavan, R. Dreicer, D. E. Hansel, and A. J. Stephenson, “Chronic Kidney Disease after Nephroureterectomy for Upper Tract Urothelial Carcinoma and Implications for the Administration of Perioperative Chemotherapy,” Cancer, Vol. 116, No. 12, pp. 2967–2973, June 2010. [52] A. S. Levey, R. Atkins, J. Coresh, E. P. Cohen, A. J. Collins, K. U. Eckardt, M. E. Nahas, B. L. Jaber, M. Jadoul, A. Levin, N. R. Powe, J. Rossert, D. C. Wheeler, N. Lameire, and G. Eknoyan, “Chronic Kidney Disease as a Global Public Health Problem: Approaches and Initiatives-A Position Statement from Kidney Disease Improving Global Outcomes,” Kidney International, Vol. 72, No. 3, pp. 247–259, Aug. 2007. [53] C. E. Li and Y. I. Chang, “A Time-Position Join Method for Periodicity Mining in Time Series Databases,” Proceedings of International Computer Symposium, pp. 294–299, 2016. [54] C. E. Li and Y. I. Chang, “Applying the Chi-Square Test to Improve the Performance of the Decision Tree for Classification by Taking Baseball Database as an Examples,” accepted by The Journal of Computers, Dec. 2017. [55] C. E. Li, C. S. Chien, Y. C. Chuang, Y. I. Chang, H. P. Tang, and C. H. Kang, “Chronic Kidney Disease as an Important Risk Factor for Tumor Recurrences, Progression and Overall Survival in Primary Non-Muscle Invasive Bladder Cancer,” International Urology and Nephrology, Vol. 48, No. 6, pp. 993–999, June 2016. [56] G. S. Linoff and M. J. A. Berry, “Data Mining Techniques: For Marketing, Sales, and Customer Support,” John Wiley and Sons Inc., 1997. [57] L. Mage, N. Baati, A. Nanchen, F. Stoessel, and T. Meyer, “A Systematic Approach for Thermal Stability Predictions of Chemicals and Their Risk Assessment: Pattern Recognition and Compounds Classification based on Thermal Decomposition Curves,” Process Safety and Environmental Protection, Vol. 110, pp. 43–52, Aug. 2017. [58] R. Mano, J. Baniel, O. Shoshany, D. Margel, T. B. M. Sc., O. Nativ, J. Rubinstein, and S. Halachmi, “Neutrophil-to-Lymphocyte Ratio Predicts Progression and Recurrence of NonvMuscle-Invasive Bladder Cancer,” Urologic Oncology: Seminars and Original Investigations, Vol. 33, No. 2, pp. 67.e1–67.e7, Feb. 2015. [59] A. Marques, U. Ekelund, and L. B. Sardinha, “Associations between Organized Sports Participation and Objectively Measured Physical Activity, Sedentary Time and Weight Status in Youth,” Journal of Science and Medicine in Sport, Vol. 19, No. 2, pp. 154–157, Feb. 2016. [60] M. L. McHugh, “The Chi-Square Test of Independence,” Biochemia Medica, Vol. 23, No. 2, pp. 143–149, June 2013. [61] W. Meredith, “Measurement Invariance, Factor Analysis and Factorial Invariance,” Psychometrika, Vol. 58, No. 4, pp. 525–543, Dec. 1993. [62] F. Michael, R. Brandon, and R. Deb, “Temporal Feature Induction for Baseball Highlight Classification,” Proceedings of the 15th ACM International Conference on Multimedia, pp. 333–336, 2007. [63] J. Mingers, “An Empirical Comparison of Pruning Methods for Decision Tree Induction,” Machine Learning, Vol. 4, No. 2, pp. 227–243, Nov. 1989. [64] T. R. Neves, M. J. Soares, P. G. Monteiro, M. S. Lima, and H. G. Monteiro, “Basaloid Squamous Cell Carcinoma in the Urinary Bladder with Small-Cell Carcinoma,” Journal of Clinical Oncology, Vol. 29, No. 15, pp. e440–e442, May 2011. [65] E. W. T. Ngai, L. Xiu, and D. C. K. Chau, “Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification,” Expert Systems with Applications, Vol. 36, No. 2, pp. 2592–2602, March 2009. [66] T. Onishi, T. Sasaki, A. Hoshina, and T. Yabana, “Continuous Saline Bladder Irrigation after Transurethral Resection Is a Prophylactic Treatment Choice for Non-Muscle Invasive Bladder Tumor,” Anticancer Research, Vol. 31, No. 4, pp. 1471–1474, April 2011. [67] J. Palou, R. J. Sylvester, O. R. Faba, R. Parada, J. A. Pena, F. Algaba, and H. Villavicencio, “Female Gender and Carcinoma in Situ in the Prostatic Urethra are Prognostic Factors for Recurrence, Progression, and Disease-Specific Mortality in T1G3 Bladder Cancer Patients Treated with Bacillus Calmette-Guerin,” European Urology, Vol. 62, No. 1, pp. 118–125, July 2011. [68] I. Piotr, K. Nick, and S. Muthukrishnan, “Identifying Representative Trends in Massive Time Series Data Sets using Sketches,” Proceedings of the 26th International Conference on Very Large Data Bases, pp. 363–372, 2000. [69] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, No. 1, pp. 81–106, March 1986. [70] J. R. Quinlan, “C4.5: Programs for Machine Learning,” Morgan Kaufmann Publishers Inc., 1993. [71] J. R. Quinlan, “Improved Use of Continuous Attributes in C4.5,” Journal of Artificial Intelligence Research, Vol. 4, No. 1, pp. 77–90, March 1996. [72] A. Rakesh and S. Ramakrishnan, “Fast Algorithms for Mining Association Rules in Large Databases,” Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499, 1994. [73] F. Rasheed, M. Alshalalfa, and R. Alhajj, “Efficient Periodicity Mining in Time Series Databases using Suffix Trees,” IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 1, pp. 79–94, Jan. 2011. [74] S. Rausch, J. Hennenlotter, T. Todenhofer, S. Aufderklamm, C. Schwentner, K. Sievert, A. Stenzl, and G. Gakis, “Impaired Estimated Glomerular Filtration Rate is a Significant Predictor for NonvMuscle-Invasive Bladder Cancer Recurrence and ProgressionxIntroducing a Novel Prognostic Model for Bladder Cancer Recurrence,” Urologic Oncology: Seminars and Original Investigations, Vol. 32, No. 8, pp. 1178–1183, Nov. 2014. [75] C. Sheng, W. Hsu, and M. L. Lee, “Mining Dense Periodic Patterns in Time Series Data,” Proceedings of the 22nd International Conference on Data Engineering, pp. 115–115, 2006. [76] C. N. Sternberg, “Are Nomograms Better than Currently Available Stage Groupings for Bladder Cancer?,” Therapeutic Advances in Urology, Vol. 24, No. 24, pp. 3819–3820, Aug. 2006. [77] M. Suknovic, B. Delibasic, M. Jovanovic, M. Vukicevic, D. Becejski-Vujaklija, and Z. Obradovic, “Reusable Components in Decision Tree Induction Algorithms,” Computational Statistics, Vol. 27, No. 1, pp. 127–148, March 2012. [78] R. J. Sylvester, A. P. M. van der Meijden, W. Oosterlinck, J. A. Witjes, C. Bouffioux, L. Denis, D. W. W. Newling, and K. Kurth, “Predicting Recurrence and Progression in Individual Patients with Stage Ta T1 Bladder Cancer Using EORTC Risk Tables: A Combined Analysis of 2596 Patients from Seven EORTC Trials,” European Urology, Vol. 49, No. 3, pp. 466–477, March 2006. [79] A. J. Templeton, M. G. McNamara, B. Seruga, F. E. Vera-Badillo, P. Aneja, A. Ocana, R. Leibowitz-Amit, G. Sonpavde, J. J. Knox, B. T. I. F. Tannock, and E. Amir, “Prognostic Role of Neutrophil-to-Lymphocyte Ratio in Solid Tumors: A Systematic Review and Meta-Analysis,” Journal of the National Cancer Institute, Vol. 106, No. 6, May 2014. [80] A. G. van der Heijden and J. A. Witjes, “Recurrence, Progression, and Follow-Up in Non-Muscle-Invasive Bladder Cancer,” European Urology Supplements, Vol. 8, No. 7, pp. 556–562, Sept. 2009. [81] N. D. Vaziri, M. V. Pahl, A. Crum, and K. Norris, “Effect of Uremia on Structure and Function of Immune System,” Journal of Renal Nutrition, Vol. 22, No. 1, pp. 149–156, Jan. 2012. [82] G. J. Wang, C. Xie, M. Lin, and H. E. Stanley, “Stock Market Contagion during the Global Financial Crisis: A Multiscale Approach,” Finance Research Letters, Vol. 22, pp. 163–168, Aug. 2017. [83] L. J. Wang, S. Y. Lee, B. T. Teh, C. K. Chuang, and J. Nortier, “Upper Tract Urothelial Carcinomas in Patients with Chronic Kidney Disease: Relationship with Diagnostic Challenge,” BioMed Research International, pp. 1–9, Aug. 2014. [84] P. Weiner, “Linear Pattern Matching Algorithms,” Proceedings of the 14th Annual Symposium on Switching and Automata Theory, pp. 1–11, 1973. [85] P. H. Weng, K. Y. Hung, H. L. Huang, J. H. Chen, P. K. Sung, and K. C. Huang, “Cancer-Specific Mortality in Chronic Kidney Disease: Longitudinal Follow-Up of a Large Cohort,” Clinical Journal of the American Society of Nephrology, Vol. 6, No. 5, pp. 1121–1128, May 2011. [86] G. Wong, A. Hayen, J. R. Chapman, A. C. Webster, J. J. Wang, P. Mitchell, and J. C. Craig, “Association of CKD and Cancer Risk in Older People,” Journal of Neuroscience Methods, Vol. 20, No. 6, pp. 1341–1350, Jun. 2009. [87] Y. H. Zhao and Y. X. Zhang, “Comparison of Decision Tree Methods for Finding Active Objects,” Advances in Space Research, Vol. 41, No. 12, pp. 1955–1959, 2008. |
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