research-article

On nested palindromes in clickstream data

Authors:

Michel Speiser,

Gianluca Antonini,

Abderrahim Labbi,

Juliana SutantoAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1460 - 1468

https://doi.org/10.1145/2339530.2339758

Published: 12 August 2012 Publication History

Abstract

In this paper we discuss an interesting and useful property of clickstream data. Often a visit includes repeated views of the same page. We show that in three real datasets, sampled from the websites of technology and consulting groups and a news broadcaster, page repetitions occur for the majority as a very specific structure, namely in the form of nested palindromes. This can be explained by the widespread use of features which are available in any web browser: the "refresh" and "back" buttons. Among the types of patterns which can be mined from sequence data, many either stumble if symbol repetitions are involved, or else fail to capture interesting aspects related to symbol repetitions. In an attempt to remedy this, we characterize the palindromic structures, and discuss possible ways of making use of them. One way is to pre-process the sequence data by explicitly inserting these structures, in order to obtain a richer output from conventional mining algorithms. Another application we discuss is to use the information directly, in order to analyze certain aspects of the website under study. We also provide the simple linear-time algorithm which we developed to identify and extract the structures from our data.

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References

[1]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB, pages 487--499, 1994.

Digital Library

[2]

R. Agrawal and R. Srikant. Mining sequential patterns. Data Engineering, International Conference on, 0:3, 1995.

Digital Library

[3]

T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD'02, pages 74--85, London, UK, UK, 2002. Springer-Verlag.

Digital Library

[4]

G. Casas-Garriga. Summarizing sequential data with closed partial orders. In Proceedings of the Fifth SIAM International Conference on Data Mining, pages 380--390, 2005.

[5]

S. Chakrabarti. Mining the Web: Discovering Knowledge from Hypertext Data. Morgan-Kauffman, 2002.

Digital Library

[6]

N. Chomsky. Three models for the description of language. Information Theory, IRE Transactions on, 2(3):113--124, september 1956.

[7]

A. Clauset, C. R. Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. SIAM Reviews, June 2007.

Digital Library

[8]

D. J. Cook and L. B. Holder. Mining Graph Data. John Wiley & Sons Inc., 2007.

Digital Library

[9]

A. Davison. Statistical Models. Cambridge University Press, The Pitt Building, Trumpington Street, Cambridge, UK, 2003.

[10]

A. Frank and A. Asuncion. UCI machine learning repository, 2010.

[11]

M. Giel-Pietraszuk, M. Hoffmann, S. Dolecka, J. Rychlewski, and J. Barciszewski. Palindromes in proteins. Journal of Protein Chemistry, 22:109--113, 2003. 10.1023/A:1023454111924.

[12]

D. Gusfield. Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge University Press, New York, NY, USA, 1997.

Digital Library

[13]

J. Han, H. Cheng, D. Xin, and X. Yan. Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 14(1), 2007.

Digital Library

[14]

R. Kolpakov and G. Kucherov. Searching for gapped palindromes. In P. Ferragina and G. Landau, editors, Combinatorial Pattern Matching, volume 5029 of Lecture Notes in Computer Science, pages 18--30. Springer Berlin / Heidelberg, 2008.

Digital Library

[15]

B. Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.

Digital Library

[16]

M. Mampaey, N. Tatti, and J. Vreeken. Tell me what i need to know: succinctly summarizing data with itemsets. In KDD, pages 573--581, 2011.

Digital Library

[17]

H. Mannila and C. Meek. Global partial orders from sequential data. In KDD'00: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 161--168. ACM, 2000.

Digital Library

[18]

H. Mannila, H. Toivonen, and I. Verkamo. Efficient algorithms for discovering association rules. pages 181--192. AAAI Press, 1994.

[19]

S. Morgenthaler. A survey of robust statistics. Statistical Methods & Applications, 16:171--172, 2007.

[20]

J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Transactions on Knowledge and Data Engineering, 16(11):2004, 2004.

Digital Library

[21]

J. Pei, H. Wang, J. Liu, K. Wang, J. Wang, and P. S. Yu. Discovering frequent closed partial orders from strings. IEEE Transactions on Knowledge and Data Engineering, 18:1467--1481, November 2006.

Digital Library

[22]

M. Speiser, G. Antonini, and A. Labbi. Ranking web-based partial orders by significance using a markov reference model. In IEEE 11th International Conference on Data Mining (ICDM), 2011.

Digital Library

[23]

X. Yan, J. Han, and R. Afshar. Clospan: Mining closed sequential patterns in large datasets. In In SDM, pages 166--177, 2003.

Digital Library

[24]

M. Zaki, N. Parimi, N. De, F. Gao, B. Phoophakdee, J. Urban, V. Chaoji, M. Hasan, and S. Salem. Towards generic pattern mining. In B. Ganter and R. Godin, editors, Formal Concept Analysis, volume 3403 of Lecture Notes in Computer Science, pages 1--20. Springer Berlin / Heidelberg, 2005.

Digital Library

Cited By

Korosi GHavasi F(2017)Moodle-based data mining potentials of MOOC systems at the University of Szeged2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)10.23919/MIPRO.2017.7973523(755-760)Online publication date: May-2017
https://doi.org/10.23919/MIPRO.2017.7973523
Brinton CBuccapatnam SChiang MPoor H(2016)Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz PerformanceIEEE Transactions on Signal Processing10.1109/TSP.2016.254622864:14(3677-3692)Online publication date: 15-Jul-2016
https://dl.acm.org/doi/10.1109/TSP.2016.2546228
Chwalinski PBelavkin RCheng X(2012)Detection of HTTP-GET attack with clustering and information theoretic measurementsProceedings of the 5th international conference on Foundations and Practice of Security10.1007/978-3-642-37119-6_4(45-61)Online publication date: 25-Oct-2012
https://dl.acm.org/doi/10.1007/978-3-642-37119-6_4

Index Terms

On nested palindromes in clickstream data
1. Information systems
  1. Information systems applications
    1. Data mining
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 August 2012

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Korosi GHavasi F(2017)Moodle-based data mining potentials of MOOC systems at the University of Szeged2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)10.23919/MIPRO.2017.7973523(755-760)Online publication date: May-2017
https://doi.org/10.23919/MIPRO.2017.7973523
Brinton CBuccapatnam SChiang MPoor H(2016)Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz PerformanceIEEE Transactions on Signal Processing10.1109/TSP.2016.254622864:14(3677-3692)Online publication date: 15-Jul-2016
https://dl.acm.org/doi/10.1109/TSP.2016.2546228
Chwalinski PBelavkin RCheng X(2012)Detection of HTTP-GET attack with clustering and information theoretic measurementsProceedings of the 5th international conference on Foundations and Practice of Security10.1007/978-3-642-37119-6_4(45-61)Online publication date: 25-Oct-2012
https://dl.acm.org/doi/10.1007/978-3-642-37119-6_4

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