Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions

Subject Areas : Transactions on Fuzzy Sets and Systems

Tzung-Pei Hong ¹ , Chun-Hao Chen ² , Yan-Kang Li ³ , Min-Thai Wu ⁴

1 - Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
2 - Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan.
3 - Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan.
4 - College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, China.

Received: 2022-05-14 Accepted : 2022-05-14 Published : 2022-11-07

Keywords: Concept drift, Data mining, Fuzzy c-means, Membership function,

Abstract :

People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been growing in recent years. Many algorithms dealt with concept-drift information and detected new market trends. This paper proposes an approach based on fuzzy c-means (FCM) to mine the concept drift of fuzzy membership functions. The proposed algorithm is subdivided into two stages. In the first stage, individual fuzzy membership functions are generated from different training databases by the proposed FCM-based approach. Then, the proposed algorithm will mine the concept-drift patterns from the sets of fuzzy membership functions in the second stage. Experiments on simulated datasets were also conducted to show the effectiveness of the approach.

References:

[1] J. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms, Springer, New York, (1981).
[2] P. B. Dongre and L. G. Malik, A review on real time data stream classification and adapting to various concept drift scenarios, IEEE International Advance Computing Conference, ITM University, 21-22 Feb., Gurgaon, India, (2014), 533-537.

[3] C. Fernandez-Basso, M. Ruiz and M. Martin-Bautista, Spark solutions for discovering fuzzy association rules in Big Data, International Journal of Approximate Reasoning, 137(07) (2021), 94-112.
[4] H. Guo, H. Lia, Q. Rena and W. Wang, Concept drift type identi cation based on multi-sliding windows, Information Sciences, (585) (2022), 1-23.
[5] M. Z. Hayat, J. Basiri, L. Seyedhossein and A. Shakery, Content-based concept drift detection for Email spam  ltering, 2010 5th International Symposium on Telecommunications, Iran Telecom Research Center, 4-6 Dec., Kish Island, Iran, (2010), 531-536.
[6] M. Z. Hayat, M. R. Hashemi, A DCT based approach for detecting novelty and concept drift in data streams, 2010 International Conference of Soft Computing and Pattern Recognition, Universite de Cergy-Pontoise, 7-10 Dec., Paris, France, (2010), 373-378.

[7] T. P. Hong, C. H. Chen, Y. L. Wu and Y. C. Lee, A GA-Based Fuzzy Mining Approach to Achieve a Trade-off  Between Number of Rules and Suitability of Membership Functions, Soft Computing, 10(11) (2006), 10911101.
[8] T. P. Hong, C. S. Kuo and S. C. Chi, Mining association rules from quantitative data, Intelligent Data Analysis, 3(5) (1999), 363-376.
[9] T. P. Hong, K. Y. Lin and S. L. Wang, Fuzzy data mining for interesting generalized association rules, Fuzzy Sets and Systems, 138(09) (2003), 255-269.
[10] T. P. Hong, J. M. T. Wu, Y. K. Li, and C. H. Chen, Generalizing Concept-Drift Patterns for Fuzzy Association Rules, Journal of Network Intelligence, 3(2) (2018), 126-137.
[11] E. Hllermeier, Fuzzy sets in machine learning and data mining, Applied Soft Computing, 11(03) (2011), 1493-1505.
[12] C. I. Lee, C. J. Tsai, J. H. Wu and W. P. Yang, A Decision Tree-Based Approach to Mining the Rules of Concept Drift, Fourth International Conference on Fuzzy Systems and Knowledge Discovery Hainan University, 24-27 Aug., Haikou, China, (2007), 639-643.

[13] S. Mukkavilli and S. Shetty, Mining Concept Drifting Network Trac in Cloud Computing Environments, 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Carleton University, 13-16 May, Ottawa, ON, Canada, 05 (2012), 721-722.
[14] E. Padmalatha, C. R. K. Reddy and B. P. Rani, Classi cation of Concept Drift Data Streams, International Conference on Information Science Applications, Kyonggi University, 6-9 May, Seoul, Korea, (2014), 1-5. 
[15] A. M. Parodi and P. Bonelli, A New Approach to Fuzzy Classi er Systems, Proceedings of the 5th International Conference on Genetic Algorithms, University of Illinois Urbana-Champaign, 1 June, IL, USA, (1993), 223-230.
[16] P. D. Patil and P. Kulkarni, Adaptive Supervised Learning Model for Training Set Selection under Concept Drift Data Streams, 2013 International Conference on Cloud Ubiquitous Computing Emerging Technologies, Sri Venkateshwara College of Engineering, 15-16 Nov., Pune, India, (2013) 36-41.
[17] S. Shetty, S. K. Mukkavilli and L. H. Keel, An integrated machine learning and control theoretic model for mining concept-drifting data streams, IEEE International Conference on Technologies for Homeland Security, 15-17 Nov., Boston, USA, (2011), 75-80.

[18] H. S. Song, J.K. Kim and S. Kim, Mining the change of customer behaviour in an Internet shopping mall, Expert Systems with Applications, 21(10) (2001), 157-168.
[19] R. Srikant and R. Agrawal, Mining Quantitative Association Rules in Large Relational Tables, ACM Special Interest Group on Management of Data, 25(2) (1996), 1-12.
[20] J. Sun, H. Li and H. Adeli, Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble With Time Window in Corporate Financial Risk Prediction, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4) (2013), 801-813.
[21] B. Thuraisingham, Data mining for security applications: Mining concept-drifting data streams to detect peer to peer botnet traffic, IEEE International Conference on Intelligence and Security Informatics, 17-20 June, Taipei, Taiwan, (2008), 29-30.
[22] A. Tsymbal, The Problem of Concept Drift: De nitions and Related Work, Technical Report, (2004).
[23] D. H. Widyantoro and J. Yen, Relevant data expansion for learning concept drift from sparsely labeled data, IEEE Transactions on Knowledge and Data Engineering, 17(3) (2005), 401-412.
[24] C. C. Yang and N.K. Bose, Generating fuzzy membership function with self-organizing feature map, Pattern Recognition Letters, 27(5) (2006), 356-365.
[25] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353.
[26] A. Zhang and W. Shi, Mining signi cant fuzzy association rules with di erential evolution algorithm, Applied Soft Computing, 97 (2020), 105518.
[27] X. Zheng, P. Li, X. Hu and K. Yu, Semi-supervised classi cation on data streams with recurring concept drift and concept evolution, Knowledge-Based Systems, 215(01) (2021), 106749.