Call for Paper - September 2020 Edition
IJCA solicits original research papers for the September 2020 Edition. Last date of manuscript submission is August 20, 2020. Read More

A Distance Metric that Combines Linkage, Connectivity and Density Information for Clustering in Image Processing

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Priyanka Khandelwal, Sonal Saxena

Priyanka Khandelwal and Sonal Saxena. A Distance Metric that Combines Linkage, Connectivity and Density Information for Clustering in Image Processing. International Journal of Computer Applications 167(3):40-44, June 2017. BibTeX

	author = {Priyanka Khandelwal and Sonal Saxena},
	title = {A Distance Metric that Combines Linkage, Connectivity and Density Information for Clustering in Image Processing},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {3},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {40-44},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017914226},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Rapid development in Internet technology has generated data at high velocity in large volume and variety. It needs newer methods of analysis. Combining traditional and popular methods with specialized techniques give interesting clustering outputs and are of much use in some real life applications. This paper suggests a new dissimilarity metric to handle complex data. It combines the linkage and density information of data together. Multi-dimensional scaling summarizes the data model based on the proposed distance metric to use it for image processing. The low dimensional model obtained after dimensionality reduction can be easily clustered using standard algorithms.


  1. A. Hinneburg. and D. Keim, “An efficient approach to clustering large multimedia databases with noise”, Proceedings of the 4th ACM SIGKDD Conference, 1998, pp. 58-65.
  2. A. Rodriguez and A. Laio, “Clustering by fast search and find of density peaks”, Science, Vol. 344, 2014, pp. 1492–1496.
  3. J. Y. Chen and H. H. He, “A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data”, Information Sciences, Vol. 345, 2016, pp. 271–293
  4. A. E. Bayá and P. M. Granitto, “Clustering gene expression data with a penalized graph-based metric”, BMC Bioinformatics, vol. 12, no. 1, 2011.
  5. A. Strehl and J. Ghosh, “Cluster ensembles: A knowledge reuse framework for combining multiple partitions”, Journal of Machine Learning Research, vol. 3, pp. 583-617, 2002.
  6. S. Vega-Pons and J. Ruiz-Shulcloper, “Clustering ensemble method for heterogeneous partitions”, eds. E. Bayro-Corrochano and J.-O. Eklundh, Proceedings CIARP 2009, Vol. 5856 of Lecture Notes in Computer Science, 2009, pp. 481-488.
  7. A. Fred and A. K. Jain, “Combining multiple clusterings using evidence accumulation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, 2005, pp. 835–850.
  8. S. Mika, B. Schölkopf, A. Smola, K-R Müller, M. Scholz, and G. Rätsch, “Kernel PCA and de-noising in feature spaces”, Proceedings Of International Conference On Advances In Neural Information Processing Systems, 1999, pp. 536–542.
  9. T. F. Cox and M. A. A Cox, Multidimensional scaling (2nd ed.), Chapman & Hall/CRC, 2000.
  10. E. W. Forgy, “Cluster analysis of multivariate data: efficiency v/s interpretability of classifications”, Biometrics, Vol. 21, 1965, pp. 768–769.
  11. A. E. Bayá, M. G. Larese and P. M. Granitto, “Clustering using PK-D: A connectivity and density dissimilarity”, Expert Systems with Applications, Vol. 51, pp. 151–160, 2016.
  12. Y. Lecun and C. Cortes, “The MNIST database of handwritten digits”, 1998. Accessed June 2015.
  13. AT&T Labs, “The Olivetti faces dataset”, Cambridge: AT&T Lab, 1992. Accessed June 2015.
  14. F. Alimoglu, “Combining Multiple Classifiers for Pen-Based Handwritten Digit Recognition”, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University, 1996.


Clustering, Distance Metrics, Multi dimensional Scaling, Ensembling, density-based clustering, linkage, image processing