Abstract
In recent years, the ever increasing production of huge amounts of data has led the research community into trying to find new machine learning techniques in order to gain insight and discover hidden structures and correlation among these data. Therefore, clustering has become one of the most widely used techniques for exploratory data analysis. In this sense, this paper is proposing a new approach in hierarchical clustering; named HCuRMD, which improves the overall complexity of the whole clustering process by using a more relative perspective in defining minimal distances among different objects.
Chapter PDF
Similar content being viewed by others
References
Intel Corporation: What Happens in an Internet Minute?. http://www.intel.com
Zikopoulos, P., Eaton, C., de Ros, D., Deutch, T., Lapis, G.: Understanding Big Data, pp. 5–7. McGraw-Hill, USA (2012)
CERN: Computing. http://home.web.cern.ch/about/computing
Estivill-Castro, V.: Why so many clustering algorithms: A Position Paper. ACM SIGKDD Explorations Newsletter 4(1), 65–75 (2002)
Lloyd, P.S., Bell Telephone Laboratories.: Least square quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–137 (1982); (First Written on 1957)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231 (1996)
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: Ordering Points to Identify the Clustering Structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, pp. 49–60 (1999)
Nanopoulos, A., Theodoridis, Y., Manolopoulos, Y.: C2P: clustering based on closest pairs. In: Proceedings of the International Conference on Very Large Databases, pp. 331–340 (September 2001)
Corral, A., Manolopoulos, Y., Theodoridis, Y., Vassilakopoulos, M.: Algorithms for processing K-closest-pair queries in spatial databases. Data & Knowledge Engineering 49(1), 67–104 (2004)
Rokach, L., Oded, M.: Clustering methods. In: Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer, USA (2005)
SAS Institute Inc.: The CLUSTER Procedure: Clustering Methods, SAS/STAT 9.2 Users Guide, 2nd edn (2009). http://support.sas.com/documentation
Sibson, R.: SLINK: an optimally efficient algorithm for the single-link cluster method. The Computer Journal 16(1), 30–34 (1973)
Defays, D.: An efficient algorithm for a complete link method. The Computer Journal 20(4), 364–366 (1977)
Eppstein, D.: Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs. In: Proc. Symposium on Discrete Algorithms, SODA 1998 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Goulas, C., Chondrogiannis, D., Xenakis, T., Xenakis, A., Nanopoulos, P. (2015). HCuRMD: Hierarchical Clustering Using Relative Minimal Distances. In: Chbeir, R., Manolopoulos, Y., Maglogiannis, I., Alhajj, R. (eds) Artificial Intelligence Applications and Innovations. AIAI 2015. IFIP Advances in Information and Communication Technology, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-319-23868-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-23868-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23867-8
Online ISBN: 978-3-319-23868-5
eBook Packages: Computer ScienceComputer Science (R0)