Modern Hierarchical, Agglomerative Clustering Algorithm Based on Various - Widths

Authors(3) :-M. Srilekha, K. Harish, B. Phani Krishna

Hierarchicalclusteringalgorithms are either top-down or base up. Base up algorithms regard each document as a singleton cluster. In this paper proposed a tentatively assess the execution of various worldwide standard capacities with regards to hierarchical agglomerative clusteringalgorithms and think about the clustering aftereffects of segment algorithms for every last one of the measure capacities The proposed strategy fabricates the arrangement by at first allotting every point to its own particular cluster and afterward more than once choosing and merging pairs of clusters, to acquire a solitary comprehensive cluster. The key parameter in agglomerative algorithms is the strategy used to decide the match of clusters to be converged at each progression. Exploratory outcomes acquired on manufactured and genuine datasets show the adequacy of the proposed different width cluster strategy.

Authors and Affiliations

M. Srilekha
Department of MCA Narayana Engineering College Nellore, India
K. Harish
Department of MCA Narayana Engineering College Nellore, India
B. Phani Krishna
Department of MCA Narayana Engineering College Nellore, India

Change detection (CD), hierarchical clustering, hyperspectral (HS) images, multiple changes, multitemporalanalysis, remote sensing.

  1. P. Cunningham and S. J. Delany, “k-nearest neighbour classifiers,” Multiple Classifier Systems, pp. 1–17, 2007.
  2. A. Almalawi, X. Yu, Z. Tari, A. Fahad, and I. Khalil, “An unsupervised anomaly-based detection approach for integrity attacks onscadasystems,” Comput. Security, vol. 46, pp. 94–110, 2014.
  3. A. Shintemirov,W. Tang, and Q. H. Wu, “Power transformer fault classification based on dissolved gas analysis byimplementingbootstrap and genetic programming,” IEEE Trans. Syst., Man Cybern. C, Appl. Rev., vol. 39, no. 1, pp. 69–79, 2009.
  4. M. V. Mahoney and P. K. Chan, “An analysis of the 1999 darpa/lincoln laboratory evaluation data for network anomaly detection,” inProc. 6th Int. Symp. Recent Adv. Intrusion Detection, 2003, pp. 220–237
  5. B. S. Kim and S. B. Park, “A fast k nearest neighbour finding algorithm based on the ordered partition,” IEEE Trans. Pattern Anal. Mach.Intell., vol. TPAMI-8, no. 6, pp. 761–766, Jun. 1986.
  6. G. Shakhnarovich, T. Darrell, and P. Indyk, “Nearest-neighbour methods in learning and vision,” IEEE Trans. Neural Netw.,vol. 19, no. 2,p. 377, Feb. 2008.
  7. F. Angiulli and F. Fassetti, “DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets,” ACMTrans.Knowl. Discovery Data, vol. 3, no. 1, p. 4, 2009.
  8. F. Angiulli, S. Basta, and C. Pizzuti, “Distance-based detection and prediction of outliers,” IEEE Trans. Knowl. Data Eng., vol. 18, no. 2,pp. 145–160, Feb. 2006.
  9. A. Ghoting, S. Parthasarathy, and M. E. Otey, “Fast mining of distance-based outliers in high-dimensional datasets,” DataMining Knowl.Discovery, vol. 16, no. 3, pp. 349–364, 2008.
  10. S. D. Bay and M. Schwabacher, “Mining distance-based outliers in near linear time with randomization and a simple pruning rule,”inProc. 9th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2003, pp. 29–38.
  11. M. V. Mahoney and P. K. Chan, “An analysis of the 1999 darpa/lincoln laboratory evaluation data for network anomaly detection,” in Proc. 6th Int. Symp. Recent Adv. Intrusion Detection, 2003, pp. 220–237.
  12. N. Roussopoulos, S. Kelley, and F. Vincent, “Nearest neighbour queries,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, San Jose, CA, USA, May. 22–25, 1995, pp. 71–79.
  13. G. R. Hjaltason and H. Samet, “Distance browsing in spatial databases,” ACM Trans. Database Syst., vol. 24, no. 2, pp. 265–318, 1999.
  14. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proc. Int. Joint Conf. Artif. Intell., 1995, vol. 14. pp. 1137–1145.
  15. A. Frank and A. Asuncion. (2013). UCI machine learning repository [Online]. Available: http://archive.ics.uci.edu/ml
  16. A. Almalawi, Z. Tari, I. Khalil, and A. Fahad, “SCADAVT-a framework for SCADA security testbed based on virtualization technology,” in Proc. IEEE 38th Conf. Local Comput. Netw., 2013, pp. 639–646.
  17. A. Vergara, S. Vembu, T. Ayhan, M. A. Ryan, M. L. Homer, and R. Huerta, “Chemical gas sensor drift compensation using classifier ensembles,” Sens. Actuators B: Chemical, vol. 166, pp. 320–329, 2012.
  18. I. Rodriguez-Lujan, J. Fonollosa, A. Vergara, M. Homer, and R. Huerta, “On the calibration of sensor arrays for pattern recognition using the minimal number of experiments,” Chemometrics Intell. Laboratory Syst., vol. 130, pp. 123–134, 2014.

Publication Details

Published in : Volume 3 | Issue 4 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1261-1265
Manuscript Number : CSEIT1833663
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

M. Srilekha, K. Harish, B. Phani Krishna, "Modern Hierarchical, Agglomerative Clustering Algorithm Based on Various - Widths ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.1261-1265, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833663

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