A New Method for Ranking the Discovered Rules Obtained from Data Mining Using Data Envelopment Analysis
Subject Areas : Industrial Management
1 - Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran
Keywords:
Abstract :
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.
References:
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.
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.
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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.
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
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
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
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.
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.
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
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
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
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
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
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
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
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.
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
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
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
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
Guy, Ido. (2014). Tutorial on social recommender systems. Paper presented at the Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea.
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.
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
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
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.
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
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).
Kincade, K. (1998). Data mining: digging for healthcare gold. Insurance & Technology, 23(2), 2-7.
Koh, Hian Chye, & Tan, Gerald. (2011). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 65.
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.
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.
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
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
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
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
Liu, Jianqi, Wan, Jiafu, He, Shenghua, & Zhang, Yanlin. (2014). E-Healthcare Supported by Big Data. ZTE Communications, 12(3), 46-52.
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
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
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
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
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
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
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
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.
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
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
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
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
Resnick, Paul, & Varian, Hal R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58. doi: 10.1145/245108.245121
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
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
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.
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.
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
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.
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
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.
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.
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
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
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
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
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
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
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
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
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.
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.
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.
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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.
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
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
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
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.
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.
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
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
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
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
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
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
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
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.
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
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
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
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
Guy, Ido. (2014). Tutorial on social recommender systems. Paper presented at the Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea.
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.
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
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
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.
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
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).
Kincade, K. (1998). Data mining: digging for healthcare gold. Insurance & Technology, 23(2), 2-7.
Koh, Hian Chye, & Tan, Gerald. (2011). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 65.
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.
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.
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
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
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
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
Liu, Jianqi, Wan, Jiafu, He, Shenghua, & Zhang, Yanlin. (2014). E-Healthcare Supported by Big Data. ZTE Communications, 12(3), 46-52.
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
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
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
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
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
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
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
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.
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
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
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
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
Resnick, Paul, & Varian, Hal R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58. doi: 10.1145/245108.245121
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
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
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.
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.
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
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.
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
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.
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.
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
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
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
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
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
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
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
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
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.
_||_