Re: Why is the rule output of the ERMiner algorithm so strange?
Date: February 17, 2022 03:05AM
I see. Yes, as long as you have at least one sequence, you can find rules. That is correct.
Yes, in some papers, we dont use a lot of sequences.
For example, during my PhD thesis, over 10 years ago, I was using sequential pattern mining in e-learning, where we only had about 30 sequences ( http://www.philippe-fournier-viger.com/TLT-2012_FournierViger_preprint.pdf ). This was not statistically significant probably, but it still allowed to help students who were using the e-learning system.
I think if you do a quick search for papers on sequential rule mining that have cited the ERMiner and RuleGrowth algorithm, you may see quickly how many sequences they have used. Maybe you could then tell the reviewer that other studies have used that many sequences like X, Y and Z
Here are some papers about applications of sequential rule mining for example:
Quality control
Bogon, T., Timm, I. J., Lattner, A. D., Paraskevopoulos, D., Jessen, U., Schmitz,
M., Wenzel, S., Spieckermann, S.: Towards Assisted Input and Output Data Analysis
in Manufacturing Simulation: The EDASIM Approach. In: Proc. 2012 Winter
Simulation Conference, pp. 257–269 (2012)
Web page prefetching
Fournier-Viger, P. Gueniche, T., Tseng, V.S.: Using Partially-Ordered Sequential
Rules to Generate More Accurate Sequence Prediction. Proc. 8th International Conference
on Advanced Data Mining and Applications, pp. 431-442, Springer (2012)
Anti-pattern detection in service based
systems,
Nayrolles, M., Moha, N., Valtchev, P.: Improving SOA antipatterns detection in
Service Based Systems by mining execution traces. In: Proc. 20th IEEE Working
Conference on Reverse Engineering, pp. 321-330 (2013)
Recommendation
Jannach, Dietmar, and Simon Fischer. “Recommendation-based modeling support for data mining processes.” Proceedings of the 8th ACM Conference on Recommender systems. ACM, 2014.
Interestingly, the above work found that sequential rules found by CMRules provided better results than other compared patterns found using FPGrowth and other algorithms.
Jannach, D., Jugovac, M., & Lerche, L. (2015, March). Adaptive Recommendation-based Modeling Support for Data Analysis Workflows. In Proceedings of the 20th International Conference on Intelligent User Interfaces (pp. 252-262). ACM.
Restaurant recommendation
Han, M., Wang, Z., Yuan, J.: Mining Constraint Based Sequential Patterns and
Rules on Restaurant Recommendation System. Journal of Computational Information
Systems 9(10), 3901-3908 (2013)
Customer behavior analysis
Noughabi, Elham Akhond Zadeh, Amir Albadvi, and Behrouz Homayoun Far. “How Can We Explore Patterns of Customer Segments’ Structural Changes? A Sequential Rule Mining Approach.” Information Reuse and Integration (IRI), 2015 IEEE International Conference on. IEEE, 2015.
E-learning
Fournier-Viger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E.: CMRules: Mining
Sequential Rules Common to Several Sequences. Knowledge-based Systems, Elsevier,
25(1): 63-76 (2012)
Toussaint, Ben-Manson, and Vanda Luengo. “Mining surgery phase-related sequential rules from vertebroplasty simulations traces.” Artificial Intelligence in Medicine. Springer International Publishing, 2015. 35-46.
Faghihi, Usef, Philippe Fournier-Viger, and Roger Nkambou. “CELTS: A Cognitive Tutoring Agent with Human-Like Learning Capabilities and Emotions.” Intelligent and Adaptive Educational-Learning Systems. Springer Berlin Heidelberg, 2013. 339-365.
Embedded systems
Leneve, O., Berges, M., Noh, H. Y.: Exploring Sequential and Association Rule
Mining for Pattern-based Energy Demand Characterization. In: Proc. 5th ACM
Workshop on Embedded Systems For Energy-Efficient Buildings. ACM, pp. 1–2
(2013)
Alarm sequence analysis
Celebi, O.F., Zeydan, E., Ari, I., Ileri, O., Ergut, S.: Alarm Sequence Rule Mining
Extended With A Time Confidence Parameter. In: Proc. 14th Industrial Conference
on Data Mining (2014)
Ileri, Omer, and Salih Ergüt. “Alarm Sequence Rule Mining Extended With A Time Confidence Parameter.” (2014).
Manufacturing simulation
Kamsu-Foguem, B., Rigal, F., Mauget, F.: Mining association rules for the quality
improvement of the production process. Expert Systems and Applications 40(4),
1034-1045 (2012)
There are certianly others...
Edited 3 time(s). Last edit at 02/17/2022 03:08AM by webmasterphilfv.