روش جدیدی برای رتبه‌بندی قواعد حاصل از داده‌کاوی با استفاده از تحلیل پوششی داده‌ها

محورهای موضوعی : مدیریت صنعتی

Hossein Azizi ¹ (Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran)

تاریخ دریافت : 1395/04/11 تاریخ پذیرش : 1395/08/19 تاریخ انتشار : 1395/09/20

کلید واژه: Data envelopment analysis, کارآیی‌های خوشبینانه و بدبینانه, Optimistic and pessimistic efficiencies, Data mining, تحلیل پوششی داده‌ها, داده‌کاوی, قاعده‌ی انجمنی, جالب بودن, DEA with Double Frontiers, Association Rule, Interestingness, DEA با مرز دوگانه,

چکیده مقاله :

تکنیک‌های داده‌کاوی، یعنی استخراج الگوها از پایگاه‌های داده‌ای بزرگ، در تجارت به صورت گسترده‌ای مورد استفاده قرار می‌گیرند. با استفاده از این تکنیک‌ها ممکن است قواعد زیادی حاصل شوند و فقط تعداد کمی از آنها به دلیل محدودیت بودجه و منابع برای پیاده‌سازی در نظر گرفته شوند. ارزیابی و رتبه‌بندی جالب بودن و مفید بودن قواعد انجمنی در داده‌کاوی اهمیت زیادی دارد. در مطالعات قبلی که در مورد شناسایی قواعد انجمنی جالب از نظر ذهنی انجام شده است، اکثر روش‌ها مستلزم وارد کردن دستی یا پرسیدن از کاربر برای افتراق صریح قواعد جالب از ناجالب بوده است. این روش‌ها نیازمند محاسبات بسیار زیادی هستند و حتی ممکن است به نتیجه‌گیری‌های ناسازگار منتهی شوند. برای غلبه بر این مشکلات، این مقاله پیشنهاد می‌کند که از رویکرد تحلیل پوششی داده‌ها (DEA) با مرز دوگانه برای انتخاب کارآترین قاعده‌ی انجمنی استفاده شود. در این رویکرد علاوه بر بهترین کارآیی نسبی هر قاعده‌ی انجمنی، بدترین کارآیی نسبی آن نیز در نظر گرفته می‌شود. در مقایسه با DEAی سنتی، رویکرد DEA با مرز دوگانه می‌تواند کارآترین قاعده‌ی انجمنی را به درستی و به آسانی شناسایی کند. به عنوان یک مزیت، رویکرد پیشنهادی از نظر محاسباتی کارآمدتر از کارهای قبلی در این زمینه است. با استفاده از مثالی از تحلیل سبد بازار، قابلیت کاربرد روش مبتنی بر DEAی ما برای اندازه‌گیری کارآیی قواعد انجمنی با معیارهای چندگانه نشان داده خواهد شد.

چکیده انگلیسی:

Data mining techniques, i.e. extraction of patterns from large databases, are extensively used in business. Many rules may be obtained by these techniques and only a few of them may be considered for implementation due to the limitation of budgets and resources. Evaluating and ranking attractiveness and usefulness of the association rules is of paramount importance in data mining. In the earlier studies carried out on identifying mentally interesting association rules, most methods required writing information or asking users for explicit differentiation of interesting rules from uninteresting ones. These methods involve detailed calculations and they may even lead to inconsistent conclusions. To solve these problems, this article proposes the application of the double frontiers Data Envelopment Analysis (DEA) Approach for selecting the most effective association rule. In this approach, in addition to the best relative efficiency of each association rule, its worst relative efficiency is considered. Comparing with the traditional DEA, double frontiers DEA Approach is capable of identifying the most efficient association rule correctly and easily. As an advantage, the proposed approach is more efficient than the earlier works in this concern, as far as calculations are concerned. Applicability of our DEA-based method for measuring the efficiency of association rules will be shown by multiple criteria using an example of market basket analysis.

منابع و مأخذ:

1. Agrawal, Rakesh, Imieli, Tomasz, & Swami, Arun. (1993). Mining association rules between sets of items in large databases. Paper presented at the Proceedings of the 1993 ACM SIGMOD international conference on Management of data, Washington, D.C., USA.


2. Agrawal, Rakesh, & Srikant, Ramakrishnan. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Paper presented at the Proceedings of the 20th International Conference on Very Large Data Bases.


3. Amirteimoori, Alireza. (2007). DEA efficiency analysis: Efficient and anti-efficient frontier. Applied Mathematics and Computation, 186(1), 10-16. doi: http://dx.doi.org/10.1016/j.amc.2006.07.006


4. Amirteimoori, Alireza, Emrouznejad, Ali, & Khoshandam, Leila. (2013). Classifying flexible measures in data envelopment analysis: A slack-based measure. Measurement, 46(10), 4100-4107. doi: http://dx.doi.org/10.1016/j.measurement.2013.08.019


5. Amirteimoori, Alireza, Kordrostami, Sohrab, & Azizi, Hossein. (2016). Additive models for network data envelopment analysis in the presence of shared resources. Transportation Research Part D: Transport and Environment, 48, 411-424. doi: 10.1016/j.trd.2015.12.016


6. Archak, Nikolay, Ghose, Anindya, & Ipeirotis, Panagiotis G. (2011). Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Management Science, 57(8), 1485-1509. doi: 10.1287/mnsc.1110.1370


7. Azizi, Hossein. (2011). The interval efficiency based on the optimistic and pessimistic points of view. Applied Mathematical Modelling, 35(5), 2384-2393. doi: http://dx.doi.org/10.1016/j.apm.2010.11.055


8. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi: doi:10.1287/mnsc.30.9.1078


9. Bellazzi, Riccardo, & Zupan, Blaz. (2008). Predictive data mining in clinical medicine: Current issues and guidelines. International Journal of Medical Informatics, 77(2), 81-97. doi: http://dx.doi.org/10.1016/j.ijmedinf.2006.11.006


10. Breese, John S., Heckerman, David, & Kadie, Carl. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Madison, Wisconsin.


11. Camanho, A. S., & Dyson, R. G. (2005). Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments. European Journal of Operational Research, 161(2), 432-446. doi: http://dx.doi.org/10.1016/j.ejor.2003.07.018


12. Cao, Xin, Cong, Gao, & Jensen, Christian S. (2010). Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment, 3(1-2), 1009-1020. doi: 10.14778/1920841.1920968


13. Chadwick, Andrew, & May, Christopher. (2003). Interaction between States and Citizens in the Age of the Internet: "e-Government" in the United States, Britain, and the European Union. Governance, 16(2), 271-300. doi: 10.1111/1468-0491.00216


14. Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3-4), 181-186. doi: 10.1002/nav.3800090303


15. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi: http://dx.doi.org/10.1016/0377-2217(78)90138-8


16. Chen, H., Chung, W., Xu, J. J., Wang, G., Qin, Y., & Chau, M. (2004). Crime data mining: a general framework and some examples. Computer, 37(4), 50-56. doi: 10.1109/mc.2004.1297301


17. Chen, Hsinchun, Chung, Wingyan, Qin, Yi, Chau, Michael, Xu, Jennifer Jie, Wang, Gang, . . . Atabakhsh, Homa. (2003). Crime data mining: an overview and case studies. Paper presented at the Proceedings of the 2003 annual national conference on Digital government research, Boston, MA, USA.


18. Chen, Min. (2013). Towards smart city: M2M communications with software agent intelligence. Multimedia Tools and Applications, 67(1), 167-178. doi: 10.1007/s11042-012-1013-4


19. Chen, Min. (2014). NDNC-BAN: Supporting rich media healthcare services via named data networking in cloud-assisted wireless body area networks. Information Sciences, 284, 142-156. doi: http://dx.doi.org/10.1016/j.ins.2014.06.023


20. Chen, Min, Gonzalez, Sergio, Leung, Victor, Zhang, Qian, & Li, Ming. (2010). A 2G-RFID-based e-healthcare system. IEEE Wireless Communications, 17(1), 37-43. doi: 10.1109/mwc.2010.5416348


21. Chen, Min, Ma, Yujun, Jialun, Wang, Dung Ong, Mau, & Song, Enmin. (2013). Enabling comfortable sports therapy for patient: A novel lightweight durable and portable ECG monitoring system.


22. Chen, Min, Mau, Dung Ong, Wang, Xiaofei, & Wang, Honggang. (2013). The virtue of sharing: Efficient content delivery in Wireless Body Area Networks for ubiquitous healthcare.


23. Chen, Mu-Chen. (2007). Ranking discovered rules from data mining with multiple criteria by data envelopment analysis. Expert Systems with Applications, 33(4), 1110-1116. doi: http://dx.doi.org/10.1016/j.eswa.2006.08.007


24. Chen, Xiaogang, Skully, Michael, & Brown, Kym. (2005). Banking efficiency in China: Application of DEA to pre- and post-deregulation eras: 1993–2000. China Economic Review, 16(3), 229-245. doi: http://dx.doi.org/10.1016/j.chieco.2005.02.001


25. Choi, Duke Hyun, Ahn, Byeong Seok, & Kim, Soung Hie. (2005). Prioritization of association rules in data mining: Multiple criteria decision approach. Expert Systems with Applications, 29(4), 867-878. doi: http://dx.doi.org/10.1016/j.eswa.2005.06.006


26. Cook, Wade D., & Kress, Moshe. (1990). A Data Envelopment Model for Aggregating Preference Rankings. Management Science, 36(11), 1302-1310. doi: 10.1287/mnsc.36.11.1302


27. Du, Xiao Fang, Leung, Stephen C. H., Zhang, Jin Long, & Lai, K. K. (2013). Demand forecasting of perishable farm products using support vector machine. International Journal of Systems Science, 44(3), 556-567. doi: 10.1080/00207721.2011.617888


28. Duan, L., Street, W. N., & Xu, E. (2011). Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterprise Information Systems, 5(2), 169-181. doi: 10.1080/17517575.2010.541287


29. Edirisinghe, N. C. P., & Zhang, X. (2007). Generalized DEA model of fundamental analysis and its application to portfolio optimization. Journal of Banking & Finance, 31(11), 3311-3335. doi: http://dx.doi.org/10.1016/j.jbankfin.2007.04.008


30. Elgendy, Nada, & Elragal, Ahmed. (2014). Big Data Analytics: A Literature Review Paper. In P. Perner (Ed.), Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings (pp. 214-227). Cham: Springer International Publishing.


31. Ertay, Tijen, Ruan, Da, & Tuzkaya, Umut Rıfat. (2006). Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Information Sciences, 176(3), 237-262. doi: http://dx.doi.org/10.1016/j.ins.2004.12.001


32. Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290. doi: 10.2307/2343100


33. Friedman, Lea, & Sinuany-Stern, Zilla. (1997). Scaling units via the canonical correlation analysis in the DEA context. European Journal of Operational Research, 100(3), 629-637. doi: 10.1016/s0377-2217(97)84108-2


34. Gavalas, Damianos, Konstantopoulos, Charalampos, Mastakas, Konstantinos, & Pantziou, Grammati. (2014). Mobile recommender systems in tourism. Journal of Network and Computer Applications, 39, 319-333. doi: http://dx.doi.org/10.1016/j.jnca.2013.04.006


35. Guy, Ido. (2014). Tutorial on social recommender systems. Paper presented at the Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea.


36. Heer, Jeffrey, & Chi, Hsin-Chou. (2001). Identification of Web User Traffic Composition using Multi-Modal Clustering and Information Scent. Paper presented at the Conference on Data Mining.


37. Helbig, Natalie, Ramón Gil-García, J., & Ferro, Enrico. (2009). Understanding the complexity of electronic government: Implications from the digital divide literature. Government Information Quarterly, 26(1), 89-97. doi: http://dx.doi.org/10.1016/j.giq.2008.05.004


38. Hsieh, Nan-Chen, & Hung, Lun-Ping. (2010). A data driven ensemble classifier for credit scoring analysis. Expert Systems with Applications, 37(1), 534-545. doi: http://dx.doi.org/10.1016/j.eswa.2009.05.059


39. Huang, Shu-Meng. (2013). A Study of the Application of Data Mining on the Spatial Landscape Allocation of Crime Hot Spots. In F. Bian, Y. Xie, X. Cui & Y. Zeng (Eds.), Geo-Informatics in Resource Management and Sustainable Ecosystem: International Symposium, GRMSE 2013, Wuhan, China, November 8-10, 2013, Proceedings, Part I (pp. 274-286). Berlin, Heidelberg: Springer Berlin Heidelberg.


40. Johnes, Jill. (2006). Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993. European Journal of Operational Research, 174(1), 443-456. doi: http://dx.doi.org/10.1016/j.ejor.2005.02.044


41. Kambal, Eiman, Osman, Izzeldin, Taha, Methag, Mohammed, Noon, & Mohammed, Sara. (2013, 26-28 Aug. 2013). Credit scoring using data mining techniques with particular reference to Sudanese banks. Paper presented at the 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).


42. Kincade, K. (1998). Data mining: digging for healthcare gold. Insurance & Technology, 23(2), 2-7.


43. Koh, Hian Chye, & Tan, Gerald. (2011). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 65.


44. Koh, Hian Chye, Tan, Wei Chin, & Goh, Chwee Peng. (2006). A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques. International Journal of Business and Information, 1(1), 96–118.


45. Konstan, Joseph A., Walker, J. D., Brooks, D. Christopher, Brown, Keith, & Ekstrand, Michael D. (2014). Teaching recommender systems at large scale: evaluation and lessons learned from a hybrid MOOC. Paper presented at the Proceedings of the first ACM conference on Learning @ scale conference, Atlanta, Georgia, USA.


46. Lee, Hakyeon, Kim, Sang Gook, Park, Hyun-woo, & Kang, Pilsung. (2014). Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach. Technological Forecasting and Social Change, 86, 49-64. doi: http://dx.doi.org/10.1016/j.techfore.2013.08.020


47. Lenca, Philippe, Meyer, Patrick, Vaillant, Benoît, & Lallich, Stéphane. (2008). On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European Journal of Operational Research, 184(2), 610-626. doi: http://dx.doi.org/10.1016/j.ejor.2006.10.059


48. Liu, Bing, Hsu, Wynne, Chen, Shu, & Ma, Yiming. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15(5), 47-55. doi: 10.1109/5254.889106


49. Liu, F.-H. F., & Hsuan Peng, H. (2008). Ranking of units on the DEA frontier with common weights. Computers & Operations Research, 35(5), 1624-1637. doi: 10.1016/j.cor.2006.09.006


50. Liu, Jianqi, Wan, Jiafu, He, Shenghua, & Zhang, Yanlin. (2014). E-Healthcare Supported by Big Data. ZTE Communications, 12(3), 46-52.


51. Liu, Jianqi, Wang, Qinruo, Wan, Jiafu, Xiong, Jianbin, & Zeng, Bi. (2013). Towards Key Issues of Disaster Aid based on Wireless Body Area Networks. KSII Transactions on Internet and Information Systems, 7(5), 1014-1035. doi: 10.3837/tiis.2013.05.005


52. Liu, Jun, Pan, Jianke, Wang, Yanping, Lin, Dingkun, Shen, Dan, Yang, Hongjun, . . . Cao, Xuewei. (2013). Component analysis of Chinese medicine and advances in fuming-washing therapy for knee osteoarthritis via unsupervised data mining methods. Journal of Traditional Chinese Medicine, 33(5), 686-691. doi: http://dx.doi.org/10.1016/S0254-6272(14)60043-1


53. Liu, Qiang, Wan, Jiafu, & Zhou, Keliang. (2014). Cloud Manufacturing Service System for Industrial-Cluster-Oriented Application. Journal of Internet Technology, 15(3), 373-380. doi: 10.6138/JIT.2014.15.3.06


54. Liu, Shiang-Tai. (2008). A fuzzy DEA/AR approach to the selection of flexible manufacturing systems. Computers & Industrial Engineering, 54(1), 66-76. doi: http://dx.doi.org/10.1016/j.cie.2007.06.035


55. Lu, Chi-Jie, & Wang, Yen-Wen. (2010). Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting. International Journal of Production Economics, 128(2), 603-613. doi: http://dx.doi.org/10.1016/j.ijpe.2010.07.004


56. Maaß, Dennis, Spruit, Marco, & de Waal, Peter. (2014). Improving short-term demand forecasting for short-lifecycle consumer products with data mining techniques. Decision Analytics, 1(1), 4. doi: 10.1186/2193-8636-1-4


57. Mannino, Michael, Hong, Sa Neung, & Choi, In Jun. (2008). Efficiency evaluation of data warehouse operations. Decision Support Systems, 44(4), 883-898. doi: http://dx.doi.org/10.1016/j.dss.2007.10.011


58. Ng, Raymond T., Lakshmanan, Laks V. S., Han, Jiawei, & Pang, Alex. (1998). Exploratory mining and pruning optimizations of constrained associations rules. Paper presented at the Proceedings of the 1998 ACM SIGMOD international conference on Management of data, Seattle, Washington, USA.


59. Obata, Tsuneshi, & Ishii, Hiroaki. (2003). A method for discriminating efficient candidates with ranked voting data. European Journal of Operational Research, 151(1), 233-237. doi: http://dx.doi.org/10.1016/S0377-2217(02)00597-0


60. Olafsson, Sigurdur, Li, Xiaonan, & Wu, Shuning. (2008). Operations research and data mining. European Journal of Operational Research, 187(3), 1429-1448. doi: http://dx.doi.org/10.1016/j.ejor.2006.09.023


61. Padhy, Neelamadhab, Mishra, Pragnyaban, & Panigrahi, Rasmita. (2012). The Survey of Data Mining Applications and Feature Scope. International Journal of Computer Science, Engineering and Information Technology, 2(3), 43-58. doi: 10.5121/ijcseit.2012.2303


62. Peng, Yi, Zhang, Yong, Tang, Yu, & Li, Shiming. (2011). An incident information management framework based on data integration, data mining, and multi-criteria decision making. Decision Support Systems, 51(2), 316-327. doi: http://dx.doi.org/10.1016/j.dss.2010.11.025


63. Resnick, Paul, & Varian, Hal R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58. doi: 10.1145/245108.245121


64. Schroedl, Stefan, Wagstaff, Kiri, Rogers, Seth, Langley, Pat, & Wilson, Christopher. (2004). Mining GPS Traces for Map Refinement. Data Mining and Knowledge Discovery, 9(1), 59-87. doi: 10.1023/b:dami.0000026904.74892.89


65. Shafer, Scott M., & Byrd, Terry A. (2000). A framework for measuring the efficiency of organizational investments in information technology using data envelopment analysis. Omega, 28(2), 125-141. doi: http://dx.doi.org/10.1016/S0305-0483(99)00039-0


66. Shyam, Varan Nath. (2006). Crime Pattern Detection Using Data Mining. Paper presented at the Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology.


67. Silver, M., Sakata, T., Su, H. C, Herman, C, Dolins, S. B., & O'Shea, M. J. (2001). Case study: how to apply data mining techniques in a healthcare data warehouse. Journal of Healthcare Information Management, 15(2), 155-164.


68. Sinuany-Stern, Zilla, & Friedman, Lea. (1998). DEA and the discriminant analysis of ratios for ranking units. European Journal of Operational Research, 111(3), 470-478. doi: 10.1016/s0377-2217(97)00313-5


69. Srikant, Ramakrishnan, Vu, Quoc, & Agrawal, Rakesh. (1997). Mining association rules with item constraints. Paper presented at the Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA.


70. Sullivan, Brooke, & Mitra, Sinjini. (2014). Community Issues in American Metropolitan Cities. Journal of Cases on Information Technology, 16(1), 23-39. doi: 10.4018/jcit.2014010103


71. Sun, Jimeng, & Reddy, Chandan K. (2013). Big Data Analytics for Healthcare. Paper presented at the in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Austin.


72. Tan, Pang-Ning, Kumar, Vipin, & Srivastava, Jaideep. (2002). Selecting the right interestingness measure for association patterns. Paper presented at the Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, Edmonton, Alberta, Canada.


73. Tejeda-Lorente, A., Bernabé-Moreno, J., Porcel, C., & Herrera-Viedma, E. (2014). Integrating Quality Criteria in a Fuzzy Linguistic Recommender System for Digital Libraries. Procedia Computer Science, 31, 1036-1043. doi: http://dx.doi.org/10.1016/j.procs.2014.05.357


74. Thornton, Dallas, Mueller, Roland M., Schoutsen, Paulus, & van Hillegersberg, Jos. (2013). Predicting Healthcare Fraud in Medicaid: A Multidimensional Data Model and Analysis Techniques for Fraud Detection. Procedia Technology, 9, 1252-1264. doi: http://dx.doi.org/10.1016/j.protcy.2013.12.140


75. Wan, Jiafu, Li, Di, Zou, Caifeng, & Zhou, Keliang. (2012). M2M Communications for Smart City: An Event-Based Architecture. 895-900. doi: 10.1109/cit.2012.188


76. Wan, Jiafu, Zhang, Daqiang, Zhao, Shengjie, Yang, Laurence, & Lloret, Jaime. (2014). Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions. IEEE Communications Magazine, 52(8), 106-113. doi: 10.1109/mcom.2014.6871677


77. Wan, Jiafu, Zou, Caifeng, Ullah, Sana, Lai, Chin-Feng, Zhou, Ming, & Wang, Xiaofei. (2013). Cloud-enabled wireless body area networks for pervasive healthcare. IEEE Network, 27(5), 56-61. doi: 10.1109/mnet.2013.6616116


78. Wang, Gang, Chen, Hsinchun, & Atabakhsh, Homa. (2004). Automatically detecting deceptive criminal identities. Communications of the ACM, 47(3), 70-76. doi: 10.1145/971617.971618


79. Wang, Ying-Ming, Luo, Ying, & Liang, Liang. (2009). Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis. Journal of Computational and Applied Mathematics, 223(1), 469-484. doi: http://dx.doi.org/10.1016/j.cam.2008.01.022


80. Wang, Ying-Ming, & Yang, Jian-Bo. (2007). Measuring the performances of decision-making units using interval efficiencies. Journal of Computational and Applied Mathematics, 198(1), 253-267. doi: http://dx.doi.org/10.1016/j.cam.2005.12.025


81. Zheng, Yu, Zhang, Lizhu, Wie, Xing, & Ma, Wei-Ying. (2009). Mining interesting locations and travel sequences from GPS trajectories. Paper presented at the Proceedings of the 18th international conference on World wide web, Madrid, Spain.