[Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, 487-499, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.]Search in Google Scholar
[Ashlock, D. A., Kim, E.Y., & Guo, L. (2005). Multi-clustering: avoiding the natural shape of underlying metrics. In C. H. Dagli et al. (Eds.), Smart Engineering System Design: Vol.15. Neural Networks, Evolutionary Programming, and Artificial Life, (pp. 453-461), ASME Press.]Search in Google Scholar
[Baesens, B., Viaene, S., & Vanthienen, J. (2000) Post-processing of association rules. At The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2000). 20 - 23 Aug 2000.]Search in Google Scholar
[Bermingham, L., & Lee, I. (2014). Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Science, 29, 379-389.10.1016/j.procs.2014.05.034]Search in Google Scholar
[Bhandari, I., Colet, E., Parker, J., Pines, Z., Pratap, R., & Ramanujam, K. K. (1997). Advanced scout: Data mining and knowledge discovery in NBA data. Data Mining and Knowledge Discovery, 1(1), 121-125.10.1023/A:1009782106822]Search in Google Scholar
[Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., & Matthews, I. (2014). Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. In 2014 IEEE International Conference on Data Mining, (pp. 725-730). IEEE.10.1109/ICDM.2014.133]Search in Google Scholar
[Borrie, A., Jonsson, G. K., & Magnusson, M. S. (2002). Temporal pattern analysis and its applicability in sport: An explanation and exemplar data. Journal of Sports Sciences, 10.10.1080/02640410232067567512363299]Search in Google Scholar
[Brauckhoff, D., Dimitropoulos, X., Wagner, A., & Salamatian, K. (2012). Anomaly extraction in backbone networks using association rules. IEEE/ACM Transactions on Networking, 20(6), 1788-1799.10.1109/TNET.2012.2187306]Search in Google Scholar
[Bray, T. (2017). The JavaScript Object Notation (JSON) Data Interchange Format. RFC 8259, RFC Editor.10.17487/RFC8259]Search in Google Scholar
[Cakir, O., & Aras, M. E. (2012). A Recommendation Engine by Using Association Rules. Procedia – Social and Behavioral Sciences, 62, 452-456. World Conference on Business, Economics and Management (BEM-2012), May 4-6 2012, Antalya, Turkey.10.1016/j.sbspro.2012.09.074]Search in Google Scholar
[Cintia, P. U. d. P., Rinzivillo, S. I. N. R. C., & Pappalardo, L. U. d. P. (2015). A network-based approach to evaluate the performance of football teams. In Machine Learning and Data Mining for Sports Analytics.]Search in Google Scholar
[Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-54.]Search in Google Scholar
[Fernando, B., Fromont, E., & Tuytelaars, T. (2012). Effective use of frequent itemset mining for image classification. In Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., & Schmid, C. (Eds.), Computer Vision - ECCV 2012, (pp. 214-227)., Berlin, Heidelberg. Springer Berlin Heidelberg.10.1007/978-3-642-33718-5_16]Search in Google Scholar
[Fournier-Viger, P., & Tseng, V. S. (2011) Mining Top-K Sequential Rules. In Proc. of the 7th Intern. Conf. on Advanced Data Mining and Applications (ADMA 2011), (pp. 180-194), Springer.10.1007/978-3-642-25856-5_14]Search in Google Scholar
[Fournier-Viger P., Gueniche T., Zida S., & Tseng V.S. (2014) ERMiner: Sequential Rule Mining Using Equivalence Classes. In: Blockeel H., van Leeuwen M., Vinciotti V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham10.1007/978-3-319-12571-8_10]Search in Google Scholar
[Fournier-Viger, P., Lin, J. C.-W., Dinh, T., & Le, H. B. (2016a). Mining correlated high-utility itemsets using the bond measure. In Martinez-Alvarez, F., Troncoso, A., Quintian, H., & Corchado, E. (Eds.), Hybrid Artificial Intelligent Systems, (pp. 53-65)., Cham. Springer International Publishing.10.1007/978-3-319-32034-2_5]Search in Google Scholar
[Fournier-Viger, P., Lin, J. C.-W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., & Lam, H. T. (2016b). The spmf open-source data mining library version 2. In Berendt, B., Bringmann, B., Fromont, E., Garriga, G., Miettinen, P., Tatti, N., & Tresp, V. (Eds.), Machine Learning and Knowledge Discovery in Databases, (pp. 36-40)., Cham. Springer International Publishing.10.1007/978-3-319-46131-1_8]Search in Google Scholar
[Fournier-Viger, P., Lin, J. C. W., Vo, B., Chi, T. T., Zhang, J., & Le, H. B. (2017). A survey of itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(4), 1-41.10.1002/widm.1207]Search in Google Scholar
[Giatsis, G., & Zahariadis, P. (2008). Statistical analysis of men’s fivb beach volleyball team performance. International Journal of Performance Analysis in Sport, 8(1), 31-43.10.1080/24748668.2008.11868420]Search in Google Scholar
[Hamming, R. W. (1950). Error detecting and error correcting codes. The Bell System Technical Journal, 29(2), 147-160.10.1002/j.1538-7305.1950.tb00463.x]Search in Google Scholar
[Inokuchi, A., Washio, T., & Motoda, H. (2000). An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In Zighed, D. A., Komorowski, J., & Zytkow, J. (Eds.), Principles of Data Mining and Knowledge Discovery, (pp. 13-23)., Berlin, Heidelberg. Springer Berlin Heidelberg.10.1007/3-540-45372-5_2]Search in Google Scholar
[Jorge, A. (2004). Hierarchical Clustering for thematic browsing and summarization of large sets of Association Rules. In Proceedings of the 2004 SIAM International Conference on Data Mining, (pp. 178-187).10.1137/1.9781611972740.17]Search in Google Scholar
[Kang, B., Huh, M., & Choi, S. (2015). Performance analysis of volleyball games using the social network and text mining techniques. Journal of the Korean Data and Information Science Society, 26(3), 619-630.10.7465/jkdi.2015.26.3.619]Search in Google Scholar
[Koch, C., & Tilp, M. (2009). Beach volleyball techniques and tactics: A comparison of male and female playing characteristics. Kinesiology, 41(1), 52–59.]Search in Google Scholar
[Link, D. (2014). A toolset for beach volleyball game analysis based on object tracking. Int. J. Comp. Sci. Sport 13, 24–35]Search in Google Scholar
[Link, D. (2018). Data Analytics in Professional Soccer. Springer Vieweg, Wiesbaden.10.1007/978-3-658-21177-6]Search in Google Scholar
[Liu, Y., Liao, W.-k., & Choudhary, A. (2005). A two-phase algorithm for fast discovery of high utility itemsets. In Ho, T. B., Cheung, D., & Liu, H. (Eds.), Advances in Knowledge Discovery and Data Mining, (pp. 689-695)., Berlin, Heidelberg. Springer Berlin Heidelberg.10.1007/11430919_79]Search in Google Scholar
[Mabroukeh, N. R., & Ezeife, C. I. (2010). A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys, 43(3), 1-41.10.1145/1824795.1824798]Search in Google Scholar
[Naulaerts, S., Meysman, P., Bittremieux, W., Vu, T. N., Berghe, W. V., Goethals, B., & Laukens, K. (2015). A primer to frequent itemset mining for bioinformatics. Briefings in Bioinformatics, 2, 216-231.10.1093/bib/bbt074]Search in Google Scholar
[Ofoghi, B., Zeleznikow, J., MacMahon, C., & Raab, M. (2013). Data Mining in Elite Sports: A Review and a Framework. Measurement in Physical Education and Exercise Science, 17(3), 171-186.10.1080/1091367X.2013.805137]Search in Google Scholar
[Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.]Search in Google Scholar
[Raj, K. A. A. D., & Padma, P. (2013). Application of association rule mining: A case study on team india. In 2013 International Conference on Computer Communication and Informatics (ICCCI), (pp. 1-6). IEEE.10.1109/ICCCI.2013.6466294]Search in Google Scholar
[Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53 - 65.10.1016/0377-0427(87)90125-7]Search in Google Scholar
[Schumaker, R. P., Solieman, O. K., & Chen, H. (2010). Sports knowledge management and data mining. Annual Review of Information Science and Technology, 44(1), 115-157.10.1002/aris.2010.1440440110]Search in Google Scholar
[Sheng, L. (2013). Study of application of factors of volleyball game based on data mining. Information Technology Journal, 12(19), 5172-5176.10.3923/itj.2013.5172.5176]Search in Google Scholar
[Stöckl, M., & Morgan, S. (2013). Visualization and analysis of spatial characteristics of attacks in field hockey. International Journal of Performance Analysis in Sport, 13(1), 160-178.10.1080/24748668.2013.11868639]Search in Google Scholar
[Sun, J., Yu, W., & Zhao, H. (2010). Study of association rule mining on technical action of ball games. 2010 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010, 3, 539-542.10.1109/ICMTMA.2010.340]Search in Google Scholar
[Tan, P.-N., Kumar, V., & Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems,29(4), 293-313.10.1016/S0306-4379(03)00072-3]Search in Google Scholar
[Van Haaren, J., Ben Shitrit, H., Davis, J., & Fua, P. (2016). Analyzing volleyball match data from the 2014 world championships using machine learning techniques. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, (pp. 627-634)., New York, NY, USA. ACM.10.1145/2939672.2939725]Search in Google Scholar
[Yiannis, L. (2008). Comparison of the basic characteristics of men’s and women’s beach volleyball from the Athens 2004 Olympics. International Journal of Performance Analysis in Sport, 8668, 8.10.1080/24748668.2008.11868454]Search in Google Scholar
[Zhang, Y.-j., Zhao, H.-q., & Wu, J.-w. (2006). Research and application of data mining algorithm on technical-tactics analysis of volleyball matches. Journal of Computer Applications, 26(12), 3017-3029.]Search in Google Scholar