A dynamic split-and-merge approach for evolving cluster models

Evolving Systems

Volumn 3, Issue 3, 2012, Pages 135-151

  Lughofer, Edwin ^a  

^a JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

Author keywords

Cluster fusion and delamination; Dynamic split and merge; Evolving cluster models; Penalized Bayesian information criterion; Touching and homogeneity criteria

Indexed keywords

BAYESIAN INFORMATION CRITERION; CLUSTER FUSION; CLUSTER MODELS; SPLIT-AND-MERGE; TOUCHING AND HOMOGENEITY CRITERIA;

DATA COMMUNICATION SYSTEMS; DELAMINATION;

CLASSIFICATION (OF INFORMATION);

EID: 84865752987     PISSN: 18686478     EISSN: 18686486     Source Type: Journal    
DOI: 10.1007/s12530-012-9046-5     Document Type: Article

References (37)

1. Andrews GE, Askey R, Roy R (2001) Special functions. Cambridge University Press, Cambridge.
2. Angelov P (2004) An approach for fuzzy rule-base adaptation using on-line clustering. Int J Approx Reason 35(3): 275-289.
3. Angelov P, Filev D, Kasabov N (2010) Evolving intelligent systems-methodology and applications. Wiley, New York.
4. Angelov P, Zhou XW (2006) Evolving fuzzy systems from data streams in real-time. In: 2006 International symposium on evolving fuzzy systems (EFS'06). Ambleside, Lake District, pp 29-35.
5. Beringer J, Hüllermeier E (2006) Online clustering of parallel data streams. Data Knowl Eng 58(2): 180-204.
6. Beringer J, Hüllermeier E (2007) Adaptive optimization of the number of clusters in fuzzy clustering. In: Proceedings of the FUZZ-IEEE 2007, London, pp 1-6.
7. Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11: 1601-1604.
8. Bouchachia A (2011) Evolving clustering: an asset for evolving systems. IEEE SMC Newslett 36.
9. Bouchachia A, Vanaret C (2011) Incremental learning based on growing gaussian mixture models. In: Proceedings of 10th international conference on machine learning and applications (ICMLA 2011), Honululu, Haweii (to appear).
10. Declercq A, Piater J (2008) Online learning of gaussian mixture models-a two-level approach. In: Proceedings of the 3rd international conference on computer vision theory and applications VISAPP, Funchal, Portugal, pp 605-611.
11. Dovzan D, Skrjanc I (2011) Recursive clustering based on a Gustafson-Kessel algorithm. Evol Syst 2(1): 15-24.
12. Farnstrom F, Lewis J, Elkan C (2000) Scalability for clustering algorithms revisited. In: SIGKDD explorations, London 2(1): 51-57.
13. Gama J (2010) Knowledge discovery from data streams. Chapman & Hall/CRC, Boca Raton.
14. Gan G, Ma C, Wu J (2007) Data clustering: theory, algorithms, and applications (Asa-Siam Series on Statistics and Applied Probability). Society for Industrial & Applied Mathematics, USA.
15. Hall P, Hicks Y (2005) A method to add gaussian mixture models. Tech. rep., University of Bath.
16. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer, New York.
17. Hühn J, Hüllermeier E (2009) FR3: a fuzzy rule learner for inducing reliable classifiers. IEEE Trans Fuzzy Syst 17(1): 138-149.
18. Jain A, Dubes R (1988) Algorithms for clustering data. Prentice Hall, Upper Saddle River.
19. Jimenez L, Landgrebe D (1998) Supervised classification in high-dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data. IEEE Trans Syst Man Cybern Part C Rev Appl 28(1): 39-54.
20. Kasabov NK, Song Q (2002) DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2): 144-154.
21. Klinkenberg R (2004) Learning drifting concepts: example selection vs. example weighting. Intell Data Anal 8(3): 281-300.
22. Lima E, Hell M, Ballini R, Gomide F (2010) Evolving fuzzy modeling using participatory learning. In: Angelov P, Filev D, Kasabov N (eds) Evolving intelligent systems: methodology and applications. Wiley, New York, pp 67-86.
23. Lughofer E (2008) Extensions of vector quantization for incremental clustering. Pattern Recogn 41(3): 995-1011.
24. Lughofer E (2011) All-pairs evolving fuzzy classifiers for on-line multi-class classification problems. In: Proceedings of the EUSFLAT 2011 conference. Elsevier, Aix-Les-Bains, pp 372-379.
25. Lughofer E, Bouchot JL, Shaker A (2011) On-line elimination of local redundancies in evolving fuzzy systems. Evol Syst 2(3): 165-187.
26. Nelles O (2001) Nonlinear system identification. Springer, Berlin.
27. Orlandic R, Lai Y, Yee W (2005) Clustering high-dimensional data using an efficient and effective data space reduction. In: Proceedings of ACM conference on information and knowledge management CIKM05, pp 201-208.
28. Qin S, Li W, Yue H (2000) Recursive PCA for adaptive process monitoring. J Process Control 10(5): 471-486.
29. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2): 461-464.
30. Song M, Wang H (2005) Highly efficient incremental estimation of gaussian mixture models for online data stream clustering. In: Priddy KL (ed) Intelligent computing: theory and applications III. In: Proceedings of the SPIE, vol 5803, pp 174-183.
31. Sun H, Wang S, Jiang Q (2004) FCM-based model selection algorithm for determining the number of clusters. Pattern Recogn 37(10): 2027-2037.
32. Tabata K, Kudo MSM (2010) Data compression by volume prototypes for streaming data. Pattern Recogn 43(9): 3162-3176.
33. Vachkov G (2010) Similarity analysis and knowledge acquisition by use of evolving neural models and fuzzy decision. In: Angelov P, Filev D, Kasabov N(eds) Evolving intelligent systems: methodology and applications. Wiley, Hoboken, pp 247-272.
34. Varmuza K, Filzmoser P (2009) Introduction to multivariate statistical analysis in chemometrics. CRC Press, Boca Raton.
35. Vitter JS (1985) Random sampling with a reservoir. ACM Trans Math Softw 11: 37-57.
36. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(48): 841-847.
37. Zhao Y, Karypis G (2004) Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach Learn 55(3): 311-331.