References
- 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.
- 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.
- 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.
- Bermingham, L., & Lee, I. (2014). Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Science, 29, 379-389.
- 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.
- 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.
- 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.
- 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.
- Bray, T. (2017). The JavaScript Object Notation (JSON) Data Interchange Format. RFC 8259, RFC Editor.
- 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.
- 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.
- Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-54.
- 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.
- 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.
- 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, Cham
- 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.
- 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.
- 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.
- 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.
- Hamming, R. W. (1950). Error detecting and error correcting codes. The Bell System Technical Journal, 29(2), 147-160.
- 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.
- 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).
- 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.
- Koch, C., & Tilp, M. (2009). Beach volleyball techniques and tactics: A comparison of male and female playing characteristics. Kinesiology, 41(1), 52–59.
- Link, D. (2014). A toolset for beach volleyball game analysis based on object tracking. Int. J. Comp. Sci. Sport 13, 24–35
- Link, D. (2018). Data Analytics in Professional Soccer. Springer Vieweg, Wiesbaden.
- 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.
- Mabroukeh, N. R., & Ezeife, C. I. (2010). A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys, 43(3), 1-41.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Sheng, L. (2013). Study of application of factors of volleyball game based on data mining. Information Technology Journal, 12(19), 5172-5176.
- 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.
- 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.
- Tan, P.-N., Kumar, V., & Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems,29(4), 293-313.
- 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.
- 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.
- 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.