A Data Mining approach for forecasting failure root causes: A case study in an Automated Teller Machine (ATM) manufacturing company
Subject Areas : ManagementSeyedehpardis Bagherighadikolaei 1 , Rouzbeh Ghousi 2 , Abdolrahman Haeri 3
1 - School of Industrial Engineering, I.U.S.T
Tehran, Iran
2 - School of Industrial Engineering, I.U.S.T
Tehran, Iran
3 - School of Industrial Engineering, I.U.S.T
Tehran, Iran
Keywords:
Abstract :
Ahmed, S.R. (2001). Applications of data mining in retail business. International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004, IEEE, 455-459.
Arumawadu, H.I., Rathnayaka, R.K.T., & Illangarathne, S. (2015). Mining Profitability of Telecommunication Customers Using K-Means Clustering. Journal of Data Analysis and Information Processing, 3(3), 63.
Balaji, S., & Srivatsa, S. (2012). Customer segmentation for decision support using clustering and association rule based approaches. International Journal of Computer Science & Engineering Technology, 3(11), 525-529.
Berry, M.J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support. John Wiley & Sons, Inc.
Bhuvaneswari, C., Aruna, P., & Loganathan, D. (2014). A new fusion model for classification of the lung diseases using genetic algorithm. Egyptian Informatics Journal, 15(2), 69-77.
Chen, L-F. (2015). Exploring asymmetric effects of attribute performance on customer satisfaction using association rule method. International Journal of Hospitality Management, 47, 54-64.
Chen, Y-S., Cheng, C-H., Lai, C-J., Hsu, C-Y., & Syu, H-J. (2012). Identifying patients in target customer segments using a two-stage clustering-classification approach: A hospital-based assessment. Computers in Biology and Medicine, 42(2), 213-221.
Cheng, C-H., & Chen, Y-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36(3), 4176-2184.
Chiang, W-Y. (2011). To mine association rules of customer values via a data mining procedure with improved model: An empirical case study. Expert Systems with Applications, 38(3), 1716-1122.
Cil, I. (2012). Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10), 8611-8625.
Cimpoiu, C., Cristea, V-M., Hosu, A., Sandru, M., & Seserman, L. (2011). Antioxidant activity prediction and classification of some teas using artificial neural networks. Food chemistry, 127(3), 1323-1328.
Cremaschi, S., Shin, J., & Subramani, H.J. (2015). Data clustering for model-prediction discrepancy reduction–A case study of solids transport in oil/gas pipelines. Computers & Chemical Engineering, 81, 355-363.
da Silva, C.E.T., Filardi, V.L., Pepe, I.M., Chaves, M.A., & Santos, C.M.S. (2015). Classification of food vegetable oils by fluorimetry and artificial neural networks. Food control, 47, 86-91.
de Amorim, R.C., & Hennig, C. (2015). Recovering the number of clusters in data sets with noise features using feature rescaling factors. Information Sciences, 324, 126-145.
de Oña, J., de Oña, R., & Calvo, F.J. (2012). A classification tree approach to identify key factors of transit service quality. Expert Systems with Applications, 39(12), 11164-11171.
Dhandayudam, P., & Krishnamurthi, D.I. (2012). An improved clustering algorithm for customer segmentation. International Journal of Engineering Science and Technology, 4(2), 99-102.
Doub, A.E., Small, M.L., Levin, A., LeVangie, K., & Brick, T.R. (2016). Identifying users of traditional and Internet-based resources for meal ideas: An association rule learning approach. Appetite, 103, 128-136.
Elyasigomari, V., Mirjafari, M., Screen, H.R., & Shaheed, M.H. (2015). Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization. Applied Soft Computing, 35, 43-51.
Friedman, J., Hastie, T., & Tibshirani, R. The elements of statistical learning. (2001). Springer series in statistics, New York.
Giraud-Carrier, C., & Povel, O. (2003). Characterising data mining software. Intelligent Data Analysis,7(3), 181-192.
Golmah, V., & Mirhashemi, G. (2012). Implementing a data mining solution to customer segmentation for decayable products-a case study for a textile firm. International Journal of Database Theory and Application, 5(3), 73-90.
Günter, S., & Bunke, H. (2003). Validation indices for graph clustering. Pattern Recognition Letters. 24(8), 1107-1113.
Gurrutxaga, I., Muguerza, J., Arbelaitz, O., Pérez, J.M., & Martín, J.I. (2011). Towards a standard methodology to evaluate internal cluster validity indices. Pattern
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. 3rd edition, Elsevier.
Hosseini, S.M.S., Maleki, A., & Gholamian, M.R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264.
İşeri, A., & Karlık, B. (2009). An artificial neural networks approach on automobile pricing. Expert Systems with Applications, 36(2), 2155-2160.
Ivančević, V., Tušek, I., Tušek, J., Knežević, M., Elheshk, S., & Luković, I. (2015). Using association rule mining to identify risk factors for early childhood caries. Computer methods and programs in biomedicine, 122(2), 175-181.
Karabatak, M., & Ince, M.C. (2009). A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases. Expert Systems with Applications, 36(10), 12500-12505.
Kareem, B., & Lawal, A. (2015). Spare parts failure prediction of an automobile under criticality condition. Engineering Failure Analysis, 56, 69-79.
Kargari, M., & Sepehri, M.M. (2012). Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs. Expert Systems with Applications, 39(5), 4740-4748.
Keshavarzi, A., Sarmadian, F., Omran, E-SE., & Iqbal, M. (2015). A neural network model for estimating soil phosphorus using terrain analysis. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 127-135.
Kim, K-j., & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert systems with applications, 34(2), 1200-1209.
Kuo, R.J., & Zulvia, F.E. (2018). Automatic clustering using an improved artificial bee colony optimization for customer segmentation. Knowledge and Information Systems, 1-27.
Lee, P-J., Hu, Y-H., & Lu, K-T. (2018). Assessing the Helpfulness of Online Hotel Reviews: A Classification-based Approach. Telematics and Informatics, 35(2), 436-445.
Luo, Y., Li, Z., Guo, H., Cao, H., Song, C., Guo, X., & Zhang Y. (2017). Predicting congenital heart defects: A comparison of three data mining methods. PloS one, 12(5), e0177811.
Mahdavi, I., Cho, N., Shirazi, B., & Sahebjamnia, N. (2008). Designing evolving user profile in e-CRM with dynamic clustering of Web documents. Data & Knowledge Engineering, 65(2), 355-372.
Manolopoulou, .E, Kotsiantis, S., & Tzelepis, D. (2015). Application of association and decision rules on intellectual capital. Knowledge Management Research & Practice, 13(2), 225-234.
Martin, I., & Bestle, D. (2018). Automated eigenmode classification for airfoils in the presence of fixation uncertainties. Engineering Applications of Artificial Intelligence, 67, 187-196.
Mirabadi, A., & Sharifian, S. (2010). Application of association rules in Iranian Railways (RAI) accident data analysis. Safety Science, 48(10), 1427-1435.
Mitra, S., Pal, S.K., & Mitra, P. (2002). Data mining in soft computing framework: a survey. IEEE transactions on neural networks, IEEE, 13(1), 3-14.
Molina, C., Stepien, O., Pessegue, B., & Rameau, J-P. (2016). PKOM: A tool for clustering, analysis and comparison of big chemical collections. Digital Signal Processing, 48, 1-11.
Movagharnejad, K., Mehdizadeh, B., Banihashemi, M., & Kordkheili, M.S. (2011). Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network. Energy, 36(7), 3979-3984.
Nasibov, E., Savaş, S.K., Vahaplar, A., & Kınay, A.Ö. (2016). A survey on geographic classification of virgin olive oil with using T-operators in Fuzzy Decision Tree Approach. Chemometrics and Intelligent Laboratory Systems, 155, 86-96.
Ngai, E.W., Xiu, L., & Chau, D.C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications, 36(2), 2592-2602.
Palomares-Salas, J.C., Agüera-Pérez, A., de la Rosa, J.J.G., & Moreno-Muñoz, A. (2014). A novel neural network method for wind speed forecasting using exogenous measurements from agriculture stations. Measurement, 55, 295-304.
Parvaneh, A., Abbasimehr, H., & Tarokh, M.J. (2012). Integrating AHP and data mining for effective retailer segmentation based on retailer lifetime value. Journal of Optimization in Industrial Engineering, 5(11), 25-31.
Rumpf, T., Mahlein, A-K., Steiner, U., Oerke, E-C., Dehne, H-W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91-99.
Shaw, M.J., Subramaniam, C., Tan, G.W., & Welge, M.E. (2001). Knowledge management and data mining for marketing. Decision support systems, 31(1), 127-137.
Seret, A., vanden Broucke, S.K., Baesens, B., & Vanthienen, J. (2014). A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Systems with Applications, 41(10), 4648-4657.
Shih, Y-Y., & Liu, D-R. (2008). Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Systems with Applications, 35(1), 350-360.
Shim, B., Choi, K., & Suh, Y. (2012). CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Systems with Applications, 39(9), 7736-7742.
Silvera SAN, Mayne ST, Gammon MD, Vaughan TL, Chow W-H, Dubin JA, Dubrow, R., Stanford, J.L., West, A.B., Rotterdam, H. & Blot, W.J. (2014). Diet and lifestyle factors and risk of subtypes of esophageal and gastric cancers: classification tree analysis. Annals of epidemiology, 24(1), 50-57.
Sudha, L., Dillibabu, R., Srinivas, S.S., & Annamalai, A. (2016). Optimization of process parameters in feed manufacturing using artificial neural network. Computers and Electronics in Agriculture, 120, 1-6.
Tormo, M.T., Sanmartin, J., & Pace, J. F. (2009). Update and improvement of the traffic accident data collection procedures in Spain: The METRAS method of sequencing accident events. In 4th IRTAD Conference. Seoul, Korea. 16-17.
Tsai, C-F., & Chen, M-Y. (2010). Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Systems with Applications, 37(3), 2006-2015.
Tsiptsis, K.K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmentation, John Wiley & Sons.
Vafaie, M., Ataei, M., & Koofigar, H.R. (2014). Heart diseases prediction based on ECG signals’ classification using a genetic-fuzzy system and dynamical model of ECG signals. Biomedical Signal Processing and Control, 14, 291-296.
Van Der Schaaf, T., & Kanse, L. (2004). Biases in incident reporting databases: an empirical study in the chemical process industry. Safety Science. 42(1), 57-67.
Venkatesh, K., Ravi, V., Prinzie, A., & Van den Poel, D. (2014). Cash demand forecasting in ATMs by clustering and neural networks. European Journal of Operational Research, 232(2), 383-392.
Vermeulen-Smit, E., Ten Have, M., Van Laar, M., & De Graaf, R. (2015). Clustering of health risk behaviours and the relationship with mental disorders. Journal of affective disorders, 171, 111-119.
Videla-Cavieres, I.F., & Ríos, S.A. (2014). Extending market basket analysis with graph mining techniques: A real case. Expert Systems with Applications, 41(4), 1928-1936.
Vijayarani, S., & Sudha, S. (2015). An efficient clustering algorithm for predicting diseases from hemogram blood test samples. Indian Journal of Science and Technology, 8(17).
Wang, J-Y., Liu, C-S., Lung, C-H., Yang, Y-T., & Lin, M-H. (2017). Investigating spousal concordance of diabetes through statistical analysis and data mining. PloS one, 12(8), e0183413.
Wang, B., Miao, Y., Zhao, H., Jin, J., & Chen, Y. (2016). A biclustering-based method for market segmentation using customer pain points. Engineering Applications of Artificial Intelligence, 47, 101-109.
Weng, J., Zhu, J-Z., Yan, X., & Liu, Z. (2016). Investigation of work zone crash casualty patterns using association rules. Accident Analysis & Prevention, 92, 43-52.
Zhang, J., Feng, Q., Zhang, X., Zhang, X., Yuan, N., Wen, S., Wang, S., & Zhang, A. (2015). The use of an artificial neural network to estimate natural gas/water interfacial tension. Fuel, 157, 28-36.
Zhao, Z., Yang, Z., Lin, H., Wang, J., & Gao, S. (2016). A protein-protein interaction extraction approach based on deep neural network. International Journal of Data Mining and Bioinformatics, 15(2), 145-164.