Journal of Intelligent Manufacturing

, Volume 16, Issue 6, pp 587–597 | Cite as

An Efficient Clustering Algorithm for Patterns Placement in Walkthrough System

  • Shao-Shin Hung
  • Ting-Chia Kuo
  • Damon Shing-Min Liu


Mining of sequential patterns in walkthrough systems is an interesting data mining problem. It can be envisioned as a tool for forecasting and prediction of the future behavior of user’s traversal patterns. In the past, how to display the object faster in the next time were their concerns. They seldom consider the problem of access times of objects in the storage systems. In this paper, we will consider this problem and solve this by clustering. Clustering methodology is particularly appropriate for the exploration of interrelationships among objects to reduce the access times of objects. We record the user’s path as log-data and store it in database. After a certain period of time, we will process the log-data database for user traversal paths and find out their characteristics, which will be utilized to determine the optimal physical organization of those VRML objects on disks. Meanwhile, we also introduce the relationships among transactions, views and objects. According to these relationships, we suggest the clustering criteria--inter-pattern similarity, which utilize these characteristics to distribute the objects into the appropriate clusters. As a result, the large-scale VRML models could be accessed more efficiently, allowing for a real-time walk-through in the scene.


walkthrough system co-occurence clustering sequential patterns access time 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aggrawal R., Gehrke J., Gunopulos D., Raghawan P. (1998). Automatic subspace clustering of high dimensional data for data mining applications. Proceeding ACM SIGMOD International Conference on Management of Data, Seattle, WA. pp. 94–105.Google Scholar
  2. Agrawal, R., Tomasz I. and Arun N. Swami (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216.Google Scholar
  3. Daniel, G. A. and Anselmo, L. (1996) Automatic image placement to provide a guaranteed frame rate. Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 307–316.Google Scholar
  4. Berchtold, S., Daniel, A. K. and Hans-Peter, K. (1996) The X–tree: an Index structure for high-dimensional data. Proceedings of the 22nd International Conference on Very Large Databases, San Francisco, August 1996, pp. 28–39.Google Scholar
  5. Berkhin, P. (2002) Survey of clustering data mining techniques., Technical Report, Accrue Software.
  6. Berson, A. and Smith, S. J. (1997). Data warehousing, Data Mining & OLAP, McGraw-Hill, Inc.,Google Scholar
  7. Bradly, P., Fayyad, U. and Reina, C. (1998) Scaling clustering algorithms to large datasets. Proceeding 4th International Conference on Knowledge Discovery and Data Mining (KDD 98).Google Scholar
  8. Bradly, P., Fayyad, U. and Reina, C. (1999) Scaling EM(expectation-maximization) clustering to large databases. Technical Report MSR-TR-98-35, Microsoft Research.Google Scholar
  9. Catledge, L. D., James, E. P. 1995Characterizing browsing strategies in the world–wide webComputer Networks and ISDN systems2710651073CrossRefGoogle Scholar
  10. Chakrabarti, S. (2003) Mining the web: discovering knowledge from hypertext data, Morgan Kaufmann PublishingGoogle Scholar
  11. Ming-Syan C., Jong Soo P., Philip S. Yu. (1996) Efficient data mining for path traversal patterns. Proceedings of the 16th International Conference on Distributed Computing Systems, pp. 385–392Google Scholar
  12. Ming-Syan, C., Jong Soo, P., Philip, S.Yu 1998Efficient data mining for path traversal patternsIEEE Transactions on Knowledge and Data Engineering10209221CrossRefGoogle Scholar
  13. Dhillon, I. S., Edward, M. M., Usman, Roshan 2003Diametrical clustering for identifying anti–correlated gene clustersBioinformatics1916121619CrossRefPubMedGoogle Scholar
  14. Domingos P., Hulten G. (2002). Learning from infinite data in finite time. Advances in Neural Information Processing System. 14Google Scholar
  15. zu Eiben S. M., Stein B. (2002). Analysis of clustering algorithms for web-based search. Practical Aspects of Knowledge Management. Volume 2569 LNAI of Lecture Notes in Artificial Intelligence, Berlin Heidelberg, Springer, December 2002, pp. 168–178Google Scholar
  16. Eirinaki, M. and Michalis, V. (2003) Web mining for web personalization. ACM Transactions on Internet Technology (TOIT), 3(1), 1–27, February 2003.Google Scholar
  17. Ester M., Kriegel H. P., Sander J. and Xu X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceeding the Second International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226–231Google Scholar
  18. Martin, E., Hans-Peter, K., Jorg, S. and Xiaowei, Xu. (1998) Clustering for mining in large spatial databases. KI (Artificial Intelligence), Special Issue on Data Mining, ScienTec Publishing, pp. 18–24, March 1998Google Scholar
  19. Mathias, G. and Hatem, H. (2003) Evaluation of web usage mining approaches for user’s next request prediction. Proceedings of the Fifth ACM International Workshop on Web Information and Data Management, pp. 74–81Google Scholar
  20. Guo, D., Peuquet, D. and Gahegan, M. (2002) Opening the black box: interactive hierarchical clustering for multivariate spatial patterns. Proceedings of the Tenth ACM International Symposium on Advances in Geographic Information Systems, pp. 131–136 November 2002.Google Scholar
  21. Eui-Hong, H., George, K., Vipin, K. and Bamshad, M. (1997) Clustering based on association rules hypergraphs. Proceedings Workshop on Research Issues on Data Mining and Knowledge Discovery.Google Scholar
  22. Han, E.-H., George, K., Vipin, K. and Bamshad, M. (1998) Hypergraph based clustering in high-dimensional data sets: a summary of results. Data Engineering Bulletin of IEEE Computer Society, 21(1)Google Scholar
  23. Hinneburg, A. and Keim, D. A. (1998) An efficient approach to clustering in multimedia databases with noise. Proceeding of the 4th International Conference on Knowledge Discovery and Data Mining, New York, AAAI Press, pp. 58–65Google Scholar
  24. Hinneburg, A., Daniel, A. K. and Wawryniuk, M. (1999) Cluster discovery methods for large databases: from the past to the future. Tutorial, Proceeding ACM SIGMOD International Conference on Management of Data, Philadelphia, PA, pp. 506–517.Google Scholar
  25. Karypis, G., Eui-Hong (Sam) Han. and Vipin, K. (1999) CHAMELEON: a Hierarchical clustering algorithm using dynamic modeling. Computer, IEEE Computer Society, 32(8), 68–75Google Scholar
  26. Kaufman, L., Rousseeuw, P. J. 1990Finding Groups in Data: An Introduction to Cluster AnalysisWileyNew YorkGoogle Scholar
  27. MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, University of California Press, Berkeley, pp. 281–297Google Scholar
  28. Rosa M., Pier Luca L., Maristella M. and Roberto E. (2003) Clustering documents in a web directory. Proceedings of the fifth ACM International Workshop on Web Information and Data Management, New Orleans, Lousiana, November 2003 pp. 66–73Google Scholar
  29. Rosa, M., Pier Luca, L., Maristella, M. and Roberto, E. (2004) Integrating web conceptual modeling and web usage mining. Proceedings of the 10th ACM SIGKDD Workshop on Web Mining and Web Usage Analysis, Seattle, Washington, August 2004, pp. 428–439Google Scholar
  30. Morzy, T., Wojciechowski, M. Zakrzewicz, M. (1999) Pattern–oriented hierarchical clustering. Proceedings of the 3rd East European Conference on Advances in Databases and Information Systems (ADBIS’99), Maribor, Slovenia, LNCS 1691, Springer–VerlagGoogle Scholar
  31. Nakamura, Y., Tamada, T. 1994An efficient 3D object management and interactive walkthrough for the 3D facility management systemProceedings IECON’9419371941Google Scholar
  32. Nakamura, Y., Abe, S., Ohsawa, Y., Sakauchi, M. 1993A balanced hierarchical data structure multidimensional dada with efficient dynamic characteristicIEEE Transactions on Knowledge and Data Engineering5682694CrossRefGoogle Scholar
  33. Palmer, C. R. and Faloutsos, C. (2000) Density biased sampling: an improved method for data mining and clustering. Proceeding International Conference on Management of Data, ACM SIGMOD, pp. 82–92Google Scholar
  34. Shikholeslami, G., Chatterjee S. and Zhang A. (1998) Wave cluster: a multi-resolution clustering approach for very large spatial databases. Proceedings of the 24th International Conference on Very large Databases, New York. pp. 428–439Google Scholar
  35. Jaideep, S., Robert, C., Mukund, D., Pang–Ning, T. 2000Web usage mining: discovery and applications of usage patterns from web dataACM SIGKDD Explorations Newsletter11223January 2000Google Scholar
  36. Steinbach, M., Levent, E. and Vipin, K. (2003) Challenges of Clustering High Dimensional Data. Applications in Econophysics, Bioinformatics, and Pattern Recognition, Springer-VerlagGoogle Scholar
  37. Tantrum J., Alejandro M. and Werner S. (2002). Hierarchical model-based clustering of large datasets through fractionation and refractionation. SIGKDD’02, 183–190Google Scholar
  38. Ungar, L. H. and Foster, D. P. (1998) Clustering methods For collaborative filtering. Proceedings of the Workshop on Recommendation Systems, AAAI Press, Menlo Park CaliforniaGoogle Scholar
  39. Wang, W., Yang, J. and Muntz, R. (1997) STING: A statistical information grid approach to spatial data mining. Proceedings of the 23rd International conference on Very large Databases, Athens, Greece, pp. 186–195Google Scholar
  40. Zhang T., Ramakrishnan R. and Livny M. (1996) BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD Record, Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Vol. 25(2), pp. 103–114, Montreal, Quebec, CanadaGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Shao-Shin Hung
    • 1
  • Ting-Chia Kuo
    • 1
  • Damon Shing-Min Liu
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Chung Cheng UniversityTaiwanRepublic of China

Personalised recommendations