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題名：探勘不平衡資料集中之突顯樣式--以國道事故資料為實證研究
書刊名：資訊管理學報
作者：鄭麗珍 引用關係／李麗美
作者(外文)：Cheng, Li-chen／Lee, Li-mei
出版日期：2014
卷期：21:2
頁次：頁161-183
主題關鍵詞：關聯規則分類；突顯樣式；不平衡資料集；高速公路事故；權重支持度；Associative classification；Emerging patterns；Imbalance dataset；Freeway accident；Weight support
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:2
  • 共同引用共同引用:3
  • 點閱點閱:26
在資料探勘的分類問題中，大多數演算法都是設計在資料類別分布平均的情況下去訓練分類模型。然而，在實務應用上，資料類別分布不平衡是常見的狀況，在這樣的資料集設計的分類方法是很重要的研究議題。此外，透過分類模型所找到的規則常瑣碎複雜，透過突顯樣式探勘可以整理篩選出具有區分找出兩個類別之間的顯著差異與獨特識別的規則。然而，過去沒有相關研究在不平衡資料集上作突顯樣式探勘。本研究提出一個新的研究架構，基於關聯規則分類的方法，調整資料的權重於計算支持度，以探勘出不平衡資料集之突顯樣式，並加入不同年份間的突顯樣式變化探勘。本研究以真實之國道交通事故資料集為實證基礎，此資料為一個嚴重不平衡的資料集，死亡事故僅佔全部事故資料的百分之一比例都不到。然而，主管機關一直努力探求了解死亡事故發生原因，希望可以透過各項因應措施，增進行車安全減低死亡事故發生。本研究將透過提出之研究架構，找出一般及稀有死亡事故的肇事因子間關聯，並分析不同年度間肇事因子，找出一些重要的樣式，提供交通管理單位參考。
Traditional associative classification is used to search frequent patterns at the balance datasets. However, most real life datasets are imbalance. To discover special rare patterns from imbalance dataset is an important job. Currently, the freeway becomes the main transportation route at Taiwan. Because of the high speed and heavy traffic, accidents at highway would cause more serious injuries than other roads. The serious injury accidents are very small part among the accident data. The impact factors of these special cases are the most important issue. This study proposes a framework to explore the most significant reasons for serious accidents. The framework combines the associative classification method with the emerging patterns mining to discover rare and serious incidents. The weight of each accident is adjusted by the severity of accident. Since the rare items can be discovered by the proposed formula of calculation support. The results of an experiment that was conducted on a real accidents data demonstrated the efficacy of the proposed approach. After analysing these accidents, we provide some suggestions.
期刊論文
1.Abdelwahab, H. T.、Abdel-Aty, M. A.(2001)。Development of artificial neural network models to predict driver injury severity in traffic accidents at signalizes intersection。Transportation Research Record，1746，6-13。  new window
2.吳冠宏、吳信宏、郭廣洋(20060900)。應用分群技術於交通事故資料分析。品質學報，13(3)，305-312。new window  延伸查詢new window
3.Anderson, T. K.(2009)。Kernel density estimation and K-means clustering to profile road accident hotspots。Accident Analysis and Prevention，41，359-364。  new window
4.Cao, L.、Zhao, Y.、Zhang, C.(2008)。Mining impact-targeted activity patterns in imbalanced data。IEEE Transaction on Knowledge and Data Engineering，20(8)，1053-1066。  new window
5.Chang, L. Y.、Wang, H. Y.(2006)。Analysis of traffic injury severity: an application of non-parametric classification tree techniques。Accident Analysis and Prevention，38(5)，1019-1027。  new window
6.Chen, S. S.、Huang, C. K.(2013)。An efficient model for mining precise quantitative association rules with multiple minimum supports。International Journal of Innovative Computing, Information and Control，9(1)，207-222。  new window
7.Chong, M. M.、Abraham, A.、Paprzycki, M.(2005)。Traffic accident analysis using machine learning paradigms。Informatica，29，89-98。  new window
8.Delen, D.、Sharda, R.、Bessonov, M.(2006)。Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks。Accident Analysis and Prevention，38(3)，434-444。  new window
9.Depaire, B.、Wets, G.、Vanhoof, K.(2008)。Traffic accident segmentation by means of latent class clustering。Accident Analysis and Prevention，40，1257-1266。  new window
10.George, T.、Ioannis, K.、Ioannis, V.(2011)。PolyA-iEP: a data mining method for the effective prediction of polyadenylation sites。Expert Systems with Applications，38(10)，12398-12408。  new window
11.García-Borroto, M.、Martínez-Trinidad, J.、Carrasco-Ochoa, J.(201210)。A survey of emerging patterns for supervised classification。Artificial Intelligence Review，6，1-17。  new window
12.Hu, Y. H.、Chen, Y. L.(2006)。Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism。Decision Support Systems，42(1)，1-24。  new window
13.Hu, Y. H.、Wu, F.、Liao, Y. J.(2013)。An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports。Journal of Systems and Software，86(5)，1224-1238。  new window
14.Huang, C. K.(2013)。Discovery of fuzzy quantitative sequential patterns with multiple minimum supports and adjustable membership functions。Information Sciences，222，126-146。  new window
15.Li, J.、Wong, L.(2002)。Identifying good diagnostic gene proups from gene expression profiles using the concept of emerging patterns。Bioinformatics，18，725-734。  new window
16.Mussone, L.、Ferrari, A.、Oneta, M.(1999)。An analysis of urban collisions using an artificial intelligence model。Accident Analysis and Prevention，31(6)，705-718。  new window
17.Solomon, S.、Nguyen, H.、Liebowitz, J.、Agresti, W.(2006)。Using data mining to improve traffic safety programs。Industrial Management and Data Systems，106(5)，621-643。  new window
18.Sohn, S. Y.、Lee, S. H.(2003)。Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea。Safety Science，41，1-14。  new window
19.Thabtah, F.、Cowling, P.、Hammoud, S.(2006)。Improving rule sorting, predictive accuracy and training time in associative classification。Expert Systems with Applications，31(2)，414-426。  new window
20.Weng, C. H.(2011)。Mining fuzzy specific rare itemsets for education data。Knowledge-Based Systems，24(5)，697-708。  new window
21.Xie, Y.、Lord, D.、Zhang, Y.(2007)。Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis。Accident Analysis and Prevention，39，922-933。  new window
22.Yun, H.、Ha, D.、Hwang, B.、Ryu, K. H.(2003)。Mining association rules on significant rare data using relative support。The Journal of Systems and Software，67，181-191。  new window
23.Zhou, L.、Yau, S.(2007)。Efficient association rule mining among both frequent and infrequent items。Computers and Mathematics with Applications，54，737-749。  new window
會議論文
1.吳冠宏、吳信宏、郭廣洋(2004)。應用資料挖掘於交通事故資料分析。高雄，臺灣。72-81。  延伸查詢new window
2.Ali, K.、Manganaris, S.、Srikant, R.(1997)。Partial classification using association rules。The Third International Conference on Knowledge Discovery and Data Mining。Newport Beach, California：The AAAI Press。115-118。  new window
3.Alhammady, H.(2007)。Mining streaming emerging patterns from streaming data(會議日期: May 13-16)。Amman, Jordan。432-436。  new window
4.Ceci, M.、Appice, A.、Caruso, C.、Malerba. D.(2008)。Discovering emerging patterns for anomaly detection in network connection data。The 17th International Symposium，(會議日期: May 20-23)。Toronto。179-188。  new window
5.Dong, G.、Li, J.(1999)。Effcient mining of emerging patterns: discovering trends and differences(會議日期: August 15-18)。San Diego, CA, USA。43-52。  new window
6.Koh, Y. S.、Rountree, N.(2005)。Finding sporadic rules using apriori-inverse(會議日期: May 18-20)。Hanoi, Vietnam。97-106。  new window
7.Li, W.、Han, J.、Pei, J.(2001)。Accurate and efficient classification based on multiple class-association rules(會議日期: November 29-December 2)。San Jose, CA, USA。369-376。  new window
8.Liu, B.、Hsu, W.、Ma, Y.(1998)。Integrating classification and association rule mining(會議日期: August 27-31)。New York, USA。80-86。  new window
9.Nefti, S.、Oussalah, M.(2004)。A neural network approach for railway safety prediction(會議日期: Oct 10-13)。Hague, Netherlands。3915-3920。  new window
10.Romero, C.、Romero, J. R.、Luna, J. M.、Ventura, S.(2010)。Mining rare association rules from e-learning data(會議日期: June 11-13)。Pittsburgh, PA, USA。11-13。  new window
11.Szathmary, L.、Napoli, A.、Valtchev, P.(2007)。Towards rare itmeset mining(會議日期: 2007, October 29-31)。Patras, Greece。305-312。  new window
12.Tao, F.、Murtagh, F.、Farid, M.(2003)。Weighted association rule mining using weighted support and significance framework(會議日期: August 24-27)。Washington, DC, USA。661-666。  new window
13.Troiano, L.、Scibelli, G.、Birtolo, C.(2009)。A fast algorithm for mining rare itemsets(會議日期: November 30-December 2)。Pisa, Italy。1149-1155。  new window
14.Veloso, A.、Meira, W. Jr.、Zaki, M. J.(2006)。Lazy associative classification(會議日期: December 18-22)。Hong Kong, China。645-654。  new window
15.Wang, J.、Karypis, G.(2005)。HARMONY: efficiently mining the best rules for classification(會議日期: April 21-23)。Newport Beach, California, USA。205-216。  new window
16.Yin, X.、Han, J.(2003)。CPAR: classification based on predictive association rules(會議日期: May 1-3)。San Francisco, CA, USA。331-335。  new window
17.Liu, B.、Hsu, W.、Ma, Y.(1999)。Mining association rules with multiple minimum supports(會議日期: August 15-18)。San Diego, CA, USA。337-341。  new window
18.Agrawal, R.、Imielinski, T.、Swami, A. N.(1993)。Mining Association Rules between Sets of Items in Large Databases。The 1993 ACM SIGMOD International Conference on Management of Data，207-216。  new window
19.劉釗(200005)。讀郭店楚簡字詞札記。郭店楚簡國際學術研討會。武漢：湖北人民出版社。91。  延伸查詢new window
20.Agrawal, R.、Srikant, R.(1994)。Fast algorithms for mining association rules in large database。The 20th International Conference on Very Large Data Bases。Morgan Kaufmann Publishers Inc.。478-499。  new window
學位論文
1.陳文杰(2004)。應用資料挖掘技術於高速公路交通肇事次數之研究(碩士論文)。國立嘉義大學，嘉義市。  延伸查詢new window
2.周雍傑(2000)。以類神經網路探討都市地區肇事嚴重程度之研究(碩士論文)。國立成功大學，臺南市。  延伸查詢new window
3.黃昶斌(2004)。以類神經網路探討都市地區肇事嚴重程度(碩士論文)。國立交通大學，新竹市。  延伸查詢new window
4.黃湄清(2005)。利用資料探勘技術 於台灣地區肇事危險判別之研究(碩士論文)。國立中央大學，桃園縣。  延伸查詢new window
5.林郁志(1998)。都市地區肇事嚴重程度之分析研究-以臺南市為例(碩士論文)。國立成功大學，臺南市。  延伸查詢new window
6.陳志和(1999)。都市地區肇事嚴重程度預測模式之研究(碩士論文)。國立成功大學，臺南市。  延伸查詢new window
7.楊思瑜(2003)。小型車事故特性分析及嚴重程度預測模式之研究─以桃竹苗地區為例(碩士論文)。逢甲大學，臺中市。  延伸查詢new window
圖書
1.林大煜(1982)。臺灣地區道路交通事故分析及建立電腦資訊系統之研究。臺北市：交通部運輸研究所。  延伸查詢new window
2.蘇志哲(2003)。易肇事地點改善作業手冊之研訂。臺北市：交通部運輸研究所。  延伸查詢new window
3.Antonie, M. L.、Zaiane, O. R.、Coman, A.(2003)。Associative Classifiers for Medical Images, Mining Multimedia and Complex Data。New York, US：Springer Berlin Heidelberg。  new window
4.Han, Jiawei、Kamber, Micheline(2006)。Data Mining: Concepts and Techniques。San Francisco：Morgan Kaufmann Publishers。  new window
圖書論文
1.Chong, M. M.、Abraham, A.、Paprzycki, M.(2004)。Traffic accident analysis using decision trees and neural networks。IADIS International Conference on Applied Computing。Portugal：IADIS Press。  new window
2.Dong, G.、Li, D.、Wong, L.(2005)。The use of emerging patterns in the analysis of gene expression profiles for the diagnosis and understanding of diseases。New Generation of Data Mining Applications。New Jersey, US：IEEE Press/Wiley。  new window
 
 
 
 
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