Clustering approach; Density-based Clustering; Dynamic environments; FCM algorithm; Fuzzy C means clustering; Fuzzy clustering modeling; Fuzzy partition; Hybrid clustering; Media Technology; Electrical and Electronic Engineering; Computer Science Applications; Computer Networks and Communications; Information Systems
Abstract :
[en] In today’s dynamic environments, user feedback data are a valuable asset providing orientations about the achieved quality and possible improvements of various products or services. In this paper we will present a hybrid fuzzy clustering model combining variants of fuzzy c-means clustering and density based clustering for exploring well-structured user feedback data. Despite of the multitude of successful applications where these algorithms are applied separately, they also suffer drawbacks of various kinds. So, the FCM algorithm faces difficulties in detecting clusters of non-spherical shapes or densities and moreover it is sensitive to noise and outliers. On the other hand density-based clustering is not easily adaptable to generate fuzzy partitions. Our hybrid clustering model intertwines density-based clustering and variations of FCM intending to exploit the advantages of these two types of clustering approaches and diminishing their drawbacks. Finally we have assessed and compared our model in a real-world case study.
Precision for document type :
Critical notes/Edition
Disciplines :
Computer science
Author, co-author :
Bedalli, Erind; University of Elbasan, Elbasan, Albania ; Epoka University, Tirana, Albania
MANCELLARI, Enea ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) ; Epoka University, Tirana, Albania
Haskasa, Esteriana; UBS Investment Bank, Krakow, Poland
External co-authors :
yes
Language :
English
Title :
Exploring User Feedback Data via a Hybrid Fuzzy Clustering Model Combining Variations of FCM and Density-Based Clustering
Publication date :
26 August 2018
Main work title :
Lecture Notes on Data Engineering and Communications Technologies
Publisher :
Springer Science and Business Media Deutschland GmbH
De Oliveira, J.V., Pedrycz, W. (eds.): Advances in fuzzy clustering and its applications, pp. 32–49. Wiley (2007)
Nayak, J., Naik, B., Behera, H.S.: Fuzzy c-means (FCM) clustering algorithm: a decade review from 2000 to 2014. In: Computational Intelligence in Data Mining, vol. 2, pp. 133– 149. Springer, New Delhi (2015)
Havens, T.C., Bezdek, J.C., Leckie, C., Hall, L.O., Palaniswami, M.: Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Syst. 20(6), 1130–1146 (2012)
Ghosh, S., Dubey, S.K.: Comparative analysis of k-means and fuzzy c-means algorithms. Int. J. Adv. Comput. Sci. Appl. 4(4) (2013)
Tsai, D.M., Lin, C.C.: Fuzzy c-means based clustering for linearly and nonlinearly separable data. Patt. Recogn. 44(8), 1750–1760 (2011)
Bedalli, E., Ninka, I.: Exploring an educational system’s data through fuzzy cluster analysis. In: 11th Annual International Conference on Information Technology and Computer Science (2014)
Yu, J., Yang, M.S.: Analysis of parameter selection for Gustafson-Kessel fuzzy clustering using Jacobian matrix. IEEE Trans. Fuzzy Syst. 23(6), 2329–2342 (2015)
Ulutagay, G., Nasibov, E.: Fuzzy and crisp clustering methods based on the neighborhood concept: a comprehensive review. J. Intell. Fuzzy Syst. 23(6), 271–281 (2012)
Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study. Fuzzy Sets Syst. 161(4), 522–543 (2010)
Bedalli, E., Mançellari, E., Asilkan, O.: A heterogeneous cluster ensemble model for improving the stability of fuzzy cluster analysis. Procedia Comput. Sci. 1(102), 129–136 (2016)
Zhang, L., Lu, W., Liu, X., Pedrycz, W., Zhong, C.: Fuzzy c-means clustering of incomplete data based on probabilistic information granules of missing values. Knowl.-Based Syst. 1 (99), 51–70 (2016)
Liu, Q., Deng, M., Shi, Y., Wang, J.: A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity. Comput. Geosci. 1(46), 296–309 (2012)
Gan, J., Tao, Y.: DBSCAN revisited: mis-claim, un-fixability, and approximation. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 27 May 2015, pp. 519–530. ACM (2015)
Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN revisited, revisited: why and how you should (Still) use DBSCAN. ACM Trans. Database Syst. (TODS) 42(3), 19 (2017)
Tsiptsis, K.K., Chorianopoulos, A.: Data Mining Techniques in CRM: Inside Customer Segmentation. Wiley (2011)
Bedalli, E., Ninka, I.: Using homogeneous fuzzy cluster ensembles to address fuzzy c-means initialization drawbacks. Int. J. Sci. Eng. Res. 5(6), 465–469 (2014)
Silva Filho, T.M., Pimentel, B.A., Souza, R.M., Oliveira, A.L.: Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Syst. Appl. 42(17–18), 6315–6328 (2015)
