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

OPTICS on Sequential Data: Experiments and Test Results

International Journal of Computer Applications
© 2010 by IJCA Journal
Number 5 - Article 1
Year of Publication: 2010
Dr A.Damodaram

K.Santhisree and Dr A.Damodaram. Article:OPTICS on Sequential Data: Experiments and Test Results. International Journal of Computer Applications 11(5):1–4, December 2010. Published By Foundation of Computer Science. BibTeX

	author = {K.Santhisree and Dr A.Damodaram},
	title = {Article:OPTICS on Sequential Data: Experiments and Test Results},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {11},
	number = {5},
	pages = {1--4},
	month = {December},
	note = {Published By Foundation of Computer Science}


The Web has enormous, various and knowledgeable data for data mining research. Clustering web usage data is useful to discover interesting patterns pertaining to user traversals, behaviour and their usage characteristics. Moreover, users accesses web pages in an order in which they are interested and hence incorporating sequence nature of their usage is crucial for clustering web transactions. In this paper we present OPTICS ("Ordering Points To Identify the Clustering Structure") algorithm to find density based clusters on a web usage data on MSNBC.COM website which is a free news data website with so different categories of news).The clusters are generated by OPTICS algorithm . The average of inter cluster and intra cluster are Calculated. the results are compared with different similarity measures like Euclidean , Jaccard, projected Euclidean, cosine and fuzzy similarity Finally showed behavior of clusters that made by OPTICS algorithm on a sequential data in a web usage domain. we performed a variety of experiments in the context of density based clustering , quantify our results by the way of explanation s and list conclusions.


  • “Deepak P, Shourya Roy” IBM India Research Lab, “OPTICS on Text Data: Experiments and Test Results”.
  • “Dimitris K. Tasoulis, Gordon Ross, and Niall M. Adams “Department of Mathematics Imperial College London, “Visualising the Cluster Structure of Data Streams”.
  • “Hanzhou, Zhejiang”, “SEQOPTICS: A Protein Sequence Clustering Method”, Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on.
  • “M. Masson, T. Denoeux”, “Multidimensional scaling of fuzzy dissimilarity data”, 2002, ISSN: 0165-0114.
  • “Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander”, “Fast Hierarchical Clustering Based on Compressed Data and OPTICS” Proc. 4th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD 2000), Lyon, France.
  • Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise”. In Evangelos Simoudis, Jiawei Han, Usama M. Fayyad. Proc. 2 International Conference on Knowledge Discovery and Data Mining (KDD-96). pp.226-231.
  • “Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander”, “OPTICS: Ordering Points To Identify the Clustering Structure” Proc. ACM SIGMOD’99 Int. Conf. on Management of Data, Philadelphia PA, 1999.
  • Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara and Sandhya Dwarkadas, “Incremental and Interactive Sequence Mining”. Proc. in 8th ACM International Conference Information and Knowledge Management. Nov 1999.