A framework for representing navigational patterns as full temporal objects

ABSTRACT

Navigational patterns have applications in several areas including: web personalization, recommendation, user-profiling and clustering, etc. Most existing works on navigational pattern-discovery give little consideration to the effects of time (or temporal trends) on navigational patterns. Some recent works have proposed frameworks for partial temporal representation of navigational patterns. This paper proposes a framework that models navigational patterns as full temporal objects that may be represented as time series. Such a representation allows a rich array of analysis techniques to be applied to the data. The proposed framework also enhances the understanding and interpretation of discovered patterns, and provides a rich environment for integrating the analysis of navigational patterns with data from the underlying organizational environments and other external factors. Such integrated analysis is very helpful in understanding navigational patterns (e.g., E-commerce sites may integrate the trend analysis of navigational patterns with other market data and economic indicators). To achieve full temporal representation, this paper proposes a navigational pattern-discovery technique that is not based on pre-defined thresholds. This is a shift from existing techniques that are driven by pre-defined thresholds that can only support partial temporal representation of navigational patterns.

REFERENCES

• 1: Steffan Baron , Myra Spiliopoulou, Monitoring Change in Mining Results, Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery, p.51-60, September 05-07, 2001
• 2: BARON, S., SPILIOPOULOU, M., AND GÜNTHER, O. 2003. Efficient monitoring of patterns in data mining environments. In Proceedings of the 7th East European Conference on Advances in Databases and Information Systems (ADBIS), Dresden, Germany.
• 3: Ming-Syan Chen , Jong Soo Park , Philip S. Yu, Efficient Data Mining for Path Traversal Patterns, IEEE Transactions on Knowledge and Data Engineering, v.10 n.2, p.209-221, March 1998 [doi>10.1109/69.683753]
• 4: Dan Gusfield, Algorithms on strings, trees, and sequences: computer science and computational biology, Cambridge University Press, New York, NY, 1997
• 5: HETTICH, S., AND BAY, S.D. 1999. The UCI KDD archive {http://kdd.ics.uci.edu}. Department of Information and Computer Science, University of California, Irvine, CA.
• 6: Xiaoming Jin , Yuchang Lu , Chunyi Shi, Distribution Discovery: Local Analysis of Temporal Rules, Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, p.469-480, May 06-08, 2002
• 7: Yingjiu Li , Peng Ning , X. Sean Wang , Sushil Jajodia, Discovering calendar-based temporal association rules, Data & Knowledge Engineering, v.44 n.2, p.193-218, February 2003 [doi>10.1016/S0169-023X(02)00135-0]
• 8: LU, Y., AND EZEIFE, C.I. 2003. Position coded pre-order linked WAP-tree for web log sequential pattern mining. In Proceedings of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Seoul, Korea, 337-349.
• 9: MOBASHER, B., DAI, H., LUO, T., AND NAKAGAWA, M. 2000. Discovery and evaluation of aggregate usage profiles for web personalization. In Proceedings of the WebKDD Workshop.
• 10: Alexandros Nanopoulos , Yannis Manolopoulos, Mining patterns from graph traversals, Data & Knowledge Engineering, v.37 n.3, p.243-266, June 2001 [doi>10.1016/S0169-023X(01)00008-8]
• 11: Nan Niu , Eleni Stroulia , Mohammad El-Ramly, Understanding Web Usage for Dynamic Web-Site Adaptation: A Case Study, Proceedings of the Fourth International Workshop on Web Site Evolution (WSE'02), p.53, October 02-02, 2002
• 12: S. Parthasarathy , M. J. Zaki , M. Ogihara , S. Dwarkadas, Incremental and interactive sequence mining, Proceedings of the eighth international conference on Information and knowledge management, p.251-258, November 02-06, 1999, Kansas City, Missouri, United States [doi>10.1145/319950.320010]
• 13: Jian Pei , Jiawei Han , Behzad Mortazavi-Asl , Hua Zhu, Mining Access Patterns Efficiently from Web Logs, Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications, p.396-407, April 18-20, 2000
• 14: Ramesh R. Sarukkai, Link prediction and path analysis using Markov chains, Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking, p.377-386, June 2000, Amsterdam, The Netherlands
• 15: Kate A. Smith , Alan Ng, Web page clustering using a self-organizing map of user navigation patterns, Decision Support Systems, v.35 n.2, p.245-256, May 2003 [doi>10.1016/S0167-9236(02)00109-4]
• 16: Ramakrishnan Srikant , Rakesh Agrawal, Mining Sequential Patterns: Generalizations and Performance Improvements, Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, p.3-17, March 25-29, 1996
• 17: Jaideep Srivastava , Robert Cooley , Mukund Deshpande , Pang-Ning Tan, Web usage mining: discovery and applications of usage patterns from Web data, ACM SIGKDD Explorations Newsletter, v.1 n.2, January 2000 [doi>10.1145/846183.846188]
• 18: Zhong Su , Qiang Yang , Ye Lu , Hongjiang Zhang, WhatNext: A Prediction System for Web Requests using N-gram Sequence Models, Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1, p.214, June 19-20, 2000
• 19: Qiang Yang , Haining Henry Zhang , Tianyi Li, Mining web logs for prediction models in WWW caching and prefetching, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.473-478, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502584]
• 20: YANG, Q., WANG, H., AND ZHANG, W. 2002. Web-log mining for quantitative temporal-event prediction. IEEE Computational Intelligence Bulletin, 1, 1, IEEE, 10-18.

INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.4 COMPUTERS AND SOCIETY
      K.4.4 Electronic Commerce

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)
          Subjects: Theory and methods
      H.5.4 Hypertext/Hypermedia
          Subjects: Navigation

General Terms:
Design, Economics, Management, Theory

Keywords:
algorithms, design, human factors, navigational pattern discovery, temporal representation, web usage mining