Unsupervised varied density based clustering algorithm using spline

Pattern Recognition Letters

Volumn 93, Issue , 2017, Pages 48-57

  Louhichi, Soumaya ^a   Gzara, Mariem ^a   Ben Abdallah, Hanêne ^b  

^a Faculty of Economics and Management of Sfax   (Tunisia)

^b KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Data mining; Density based clustering; Exponential spline; Interpolation; Varied density clustering

Indexed keywords

DATA MINING; INTERPOLATION; NEAREST NEIGHBOR SEARCH;

CLUSTERING DATA; CLUSTERING RESULTS; DBSCAN ALGORITHM; DENSITY CLUSTERING; DENSITY-BASED CLUSTERING; DENSITY-BASED CLUSTERING ALGORITHMS; K-NEAREST NEIGHBORS; PRECISION AND RECALL;

CLUSTERING ALGORITHMS;

EID: 85006833540     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2016.10.014     Document Type: Article

References (30)

1. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J., OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec. 28:2 (1999), 49–60.
2. Aliotta, M., Cannata, A., Cassisi, C., Giugno, R., Montalto, P., Pulvirenti, A., DBStrata: a system for density-based clustering and outlier detection based on stratification. Proceedings of the Fourth International Conference on SImilarity Search and Applications. ACM, 2011, 107–108.
3. Bentley, J.L., Multidimensional binary search trees used for associative searching. Commun. ACM 18:9 (1975), 509–517.
4. Borah, B., Bhattacharyya, D.K., DDSC: a density differentiated spatial clustering technique. J. Comput. 3:2 (2008), 72–79.
5. Borah, B., Bhattacharyya, D.K., A clustering technique using density difference. Signal Processing, Communications and Networking, 2007. ICSCN'07. International Conference on IEEE, 2007, 585–588.
6. Bronshtein, I.N., Semendyayev, K.A., Musiol, G., Muehlig, H., Handbook of Mathematics. 4th ed., 2004, Springer-Verlag, New York.
7. Cao, F., Ester, M., Qian, W., Zhou, A., Density-based clustering over an evolving data stream with noise. 2006 SIAM Conference on Data Mining, SDM, 6, 2006, 328–339.
8. College, C.C, Semi-analytic Lattice Integration of Markov Functionnal Term Structure Model. 2009, University of Oxford.
9. Ester, M., Kriegel, H.P., Sander, J., Xu, X., A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. 2nd ACM SIGKDD, Portand, Oregon, 1996, 226–231.
10. Greville, T.N.E., Spline functions, interpolation, and numerical quadrature. Math. Methods Digital Comput. 2 (1967), 156–168.
11. Gnanadesikan, R., Methods For Statistical Data Analysis Of Multivariate Observations. 2011, John Wiley & Son, New York.
12. Hartigan, J.A., Wong, M.A., Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Soc. Series C 28:1 (1979), 100–108.
13. Hinneburg, A., Keim, D.A., An efficient approach to clustering in large multimedia databases with noise. KDD 98 (1998), 58–65.
14. Karypis, G., Han, E.H., Kumar, V., Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput. 32:8 (1999), 68–75.
15. Liu, P., Zhou, D., Wu, N., VDBSCAN: varied density based spatial clustering of applications with noise. Service Systems and Service Management, 2007 International Conference on IEEE, 2007, 1–4.
16. Mollah, E., Rahman, A., Chowdhury, R., An efficient method for subjectively choosing parameter ‘k’ automatically in VDBSCAN. IEEE(1), 2010, 38–41.
17. Parimala, M., Lopez, D., Senthilkumar, N.C., A survey on density based clustering algorithms for mining large spatial databases. Int. J. Adv. Sci. Technol. 31:1 (2011), 59–66.
18. Ram, A., Sharma, A., Jalal, A.S., Agrawal, A., Singh, R., An enhanced density based spatial clustering of applications with noise. Proceedings of IEEE International Advance Computing Conference, 2009, 1475–1478.
19. Ram, A., Jalal, S., Jalal, A.S, Kumar, M., A density based algorithm for discovering density varied clusters in large spatial databases. Int. J. Comput. Appl. 3:6 (2010), 1–4.
20. Schweikert, D.G., An interpolation curve using a spline in tension. J. Math. Phys. 45:1 (1966), 312–317.
21. Späth, D.M.H., Exponential spline interpolation. Computing 4:3 (1969), 225–233.
22. Tsai, C.F., Yeh, H.F., Npust: an efficient clustering algorithm using partition space technique for large databases. Next-Generation Applied Intelligence, 2009, Springer, Berlin Heidelberg, 787–796.
23. Tsai, C.F., Wu, C.T., GF-DBSCAN; a new efficient and effective data clustering technique for large databases. Chen, S., (eds.) WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering (No. 9), World Scientific and Engineering Academy and Society, 2009, 231–236.
24. Tsai, C., Huang, Y., DDCT: detecting density differences using a novel clustering technique. Proceedings of the 9th WSEAS International Conference on Multimedia Systems & Signal Processing, 2009, 243–248.
25. Ultsch, A., Clustering wih SOM: U* C. Proceedings of the 5th Workshop on Self-Organizing Maps, 2005, 75–82.
26. Van Rijsbergen, C.J., Information Retrieval. (2nd ed.), 1979, Butterworths, London.
27. Wolberg, G., Affly, I., Monotonic spline interpolation. Proceeding of Computer Graphics International, 1999, 188–195.
28. Xiong, C., Yufang, M., Yan, Z., Ping, W., GMDBSCAN: multi-density DBSCAN cluster based on grid. e-Business Engineering, 2008. ICEBE'08. IEEE International Conference on, 2008, 780–783 IEEE.
29. Zhang, T., Ramakrishnan, R., Livny, M., BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Record 25:2 (1996, June), 103–114.
30. Zhou, S., Zhou, A., Jin, W., Fan, Y., Qian, W., FDBSCAN: a fast DBSCAN algorithm. RUAN JIAN XUE BAO 11:6 (2000), 735–744.