Skip to main content
SpringerLink
Log in

An Introduction to Frequent Pattern Mining

  • Chapter
  • First Online:
Frequent Pattern Mining

Abstract

The problem of frequent pattern mining has been widely studied in the literature because of its numerous applications to a variety of data mining problems such as clustering and classification. In addition, frequent pattern mining also has numerous applications in diverse domains such as spatiotemporal data, software bug detection, and biological data. The algorithmic aspects of frequent pattern mining have been explored very widely. This chapter provides an overview of these methods, as it relates to the organization of this book.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. C. Aggarwal. Outlier Analysis, Springer, 2013.

    Google Scholar 

  2. C. Aggarwal. Social Sensing, Managing and Mining Sensor Data, Springer, 2013.

    Google Scholar 

  3. C. C. Aggarwal, and P. S. Yu. Online generation of Association Rules, ICDE Conference, 1998.

    Google Scholar 

  4. R. Agrawal, and R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases, VLDB Conference, pp. 487–499, 1994.

    Google Scholar 

  5. R. Agrawal, and R. Srikant. Mining Sequential Patterns, ICDE Conference, 1995.

    Google Scholar 

  6. C. C. Aggarwal, and P. S. Yu. A New Framework for Itemset Generation, ACM PODS Conference, 1998.

    Google Scholar 

  7. C. Aggarwal and P. Yu. Privacy-preserving data mining: Models and Algorithms, Springer, 2008.

    Google Scholar 

  8. C. C. Aggarwal, and H. Wang. Managing and Mining Graph Data Data. Springer 2010.

    Google Scholar 

  9. C. C. Aggarwal, and C. K. Reddy. Data Clustering: Algorithms and Applications, CRC Press, 2013.

    Google Scholar 

  10. R. Agrawal, T. Imielinski, and A. Swami. Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6), pp. 914–925, 1993.

    Article  Google Scholar 

  11. R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications, ACM SIGMOD Conference, 1998.

    Google Scholar 

  12. R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. Depth-first Generation of Long Patterns, ACM KDD Conference, 2000. Also appears as IBM Research Report, RC, 21538, 1999.

    Google Scholar 

  13. R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. A Tree Projection Algorithm for Generation of Frequent Itemsets, Journal of Parallel and Distributed Computing, 61(3), pp. 350–371, 2001. Also appears as IBM Research Report, RC 21341, 1999.

    Google Scholar 

  14. C. C. Aggarwal, N. Ta, J. Wang, J. Feng, M. Zaki. Xproj: A framework for projected structural clustering of XML documents, ACM KDD Conference, 2007.

    Google Scholar 

  15. C. C. Aggarwal, Y. Li, J. Wang, J. Feng. Frequent Pattern Mining with Uncertain Data, ACM KDD Conference, 2009.

    Google Scholar 

  16. C. Aggarwal, Y. Li, P. Yu, and R. Jin. On dense pattern mining in graph streams, VLDB Conference, 2010.

    Google Scholar 

  17. R. J. Bayardo Jr. Efficiently mining long patterns from databases. ACM SIGMOD Conference, 1998.

    Google Scholar 

  18. J.-F. Boulicaut, A. Bykowski, and C. Rigotti. Free-sets: A Condensed Representation of Boolean data for the Approximation of Frequency Queries. Data Mining and Knowledge Discovery, 7(1), pp. 5–22, 2003.

    Article  MathSciNet  Google Scholar 

  19. G. Buehrer, and K. Chellapilla. A Scalable Pattern Mining Approach to Web Graph Compression with Communities. WSDM Conference, 2009.

    Google Scholar 

  20. T. Calders, and B. Goethals. Mining all non-derivable frequent itemsets, Principles of Knowledge Discovery and Data Mining, 2006.

    Google Scholar 

  21. T. Calders, C. Rigotti, and J. F. Boulicaut. A survey on condensed representations for frequent sets. In Constraint-based mining and inductive databases, pp. 64–80, Springer, 2006.

    Google Scholar 

  22. J. H. Chang, W. S. Lee. Finding Recent Frequent Itemsets Adaptively over Online Data StreamsFinding Recent Frequent Itemsets Adaptively over Online Data Streams. ACM KDD Conference, 2003.

    Google Scholar 

  23. M. Charikar, K. Chen, and M. Farach-Colton. Finding Frequent Items in Data Streams, Automata, Languages and Programming, pp. 693–703, 2002.

    Google Scholar 

  24. M. S. Chen, J. S. Park, and P. S. Yu. Efficient data mining for path traversal patterns, IEEE Transactions on Knowledge and Data Engineering, 10(2), pp. 209–221, 1998.

    Article  Google Scholar 

  25. C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Zhu. Tools for privacy preserving distributed data mining. ACM SIGKDD Explorations Newsletter, 4(2), pp. 28–34, 2002.

    Article  Google Scholar 

  26. E. Cohen. M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J. Ullman, and C. Yang. Finding Interesting Associations without Support Pruning, IEEE TKDE, 13(1), pp. 64–78, 2001.

    Google Scholar 

  27. G. Cormode, S. Muthukrishnan. What’s hot and what’s not: tracking most frequent items dynamically, ACM TODS, 30(1), pp. 249–278, 2005.

    Article  MathSciNet  Google Scholar 

  28. J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. OSDI, pp. 137–150, 2004.

    Google Scholar 

  29. M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis. Frequent substructure-based approaches for classifying chemical compounds. IEEE TKDE., 17(8), pp. 1036–1050, 2005.

    Google Scholar 

  30. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. Information Systems, 29(4), pp. 343–364, 200–4.

    Google Scholar 

  31. M. Garofalakis, R. Rastogi, and K. Shim.: Sequential Pattern Mining with Regular Expression Constraints, VLDB Conference, 1999.

    Google Scholar 

  32. V. Guralnik, and G. Karypis. Parallel tree-projection-based sequence mining algorithms. Parallel Computing, 30(4): pp. 443–472, April 2004.

    Article  Google Scholar 

  33. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation, ACM SIGMOD Conference, 2000.

    Google Scholar 

  34. J. Han, H. Cheng, D. Xin, and X. Yan. Frequent Pattern Mining: Current Status and Future Directions, Data Mining and Knowledge Discovery, 15(1), pp. 55–86, 2007.

    Article  MathSciNet  Google Scholar 

  35. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu. FreeSpan: frequent pattern-projected sequential pattern mining. ACM KDD Conference, 2000.

    Google Scholar 

  36. J. Han, J. Pei, H. Pinto, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. ICDE Conference, 2001.

    Google Scholar 

  37. J. Han, J.-G. Lee, H. Gonzalez, X. Li. Mining Massive RFID, Trajectory, and Traffic Data Sets (Tutorial). ACM KDD Conference, 2008. Video of Tutoral Lecture at: http://videolectures.net/kdd08_han_mmrfid/

  38. H. Jeung, M. L. Yiu, X. Zhou, C. Jensen, H. Shen, Discovery of Convoys in Trajectory Databases, VLDB Conference, 2008.

    Google Scholar 

  39. R. Jin, G. Agrawal. Frequent Pattern Mining in Data Streams, Data Streams: Models and Algorithms, pp. 61–84, Springer, 2007.

    Google Scholar 

  40. R. Jin, L. Liu, and C. Aggarwal. Discovering highly reliable subgraphs in uncertain graphs. ACM KDD Conference, 2011.

    Google Scholar 

  41. G. Kuramuchi and G. Karypis. Frequent Subgraph Discovery, ICDM Conference, 2001.

    Google Scholar 

  42. A. R. Leach and V. J. Gillet. An Introduction to Chemoinformatics. Springer, 2003.

    Google Scholar 

  43. W. Lee, S. Stolfo, and P. Chan. Learning Patterns from Unix Execution Traces for Intrusion Detection, AAAI workshop on AI methods in Fraud and Risk Management, 1997.

    Google Scholar 

  44. W. Lee, S. Stolfo, and K. Mok. A Data Mining Framework for Building Intrusion Detection Models, IEEE Symposium on Security and Privacy, 1999.

    Google Scholar 

  45. J.-G. Lee, J. Han, K.-Y. Whang, Trajectory Clustering: A Partition-and-Group Framework, ACM SIGMOD Conference, 2007.

    Google Scholar 

  46. J.-G. Lee, J. Han, X. Li. Trajectory Outlier Detection: A Partition-and-Detect Framework, ICDE Conference, 2008.

    Google Scholar 

  47. J.-G. Lee, J. Han, X. Li, H. Gonzalez. TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. PVLDB, 1(1): pp. 1081–1094, 2008.

    Google Scholar 

  48. X. Li, J. Han, and S. Kim. Motion-alert: Automatic Anomaly Detection in Massive Moving Objects, IEEE Conference in Intelligence and Security Informatics, 2006.

    Google Scholar 

  49. X. Li, J. Han, S. Kim and H. Gonzalez. ROAM: Rule- and Motif-based Anomaly Detection in Massive Moving Object Data Sets, SDM Conference, 2007.

    Google Scholar 

  50. Z. Li, B. Ding, J. Han, R. Kays. Swarm: Mining Relaxed Temporal Object Moving Clusters, VLDB Conference, 2010.

    Google Scholar 

  51. C. Liu, X. Yan, H. Lu, J. Han, and P. S. Yu. Mining Behavior Graphs for “backtrace” of non-crashing bugs, SDM Conference, 2005.

    Google Scholar 

  52. B. Liu, W. Hsu, Y. Ma. Integrating Classification and Association Rule Mining, ACM KDD Conference, 1998.

    Google Scholar 

  53. S. Ma, and J. Hellerstein. Mining Partially Periodic Event Patterns with Unknown Periods, IEEE International Conference on Data Engineering, 2001.

    Google Scholar 

  54. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering Frequent Episodes in Sequences, ACM KDD Conference, 1995.

    Google Scholar 

  55. R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. ACM SIGMOD Conference, 1998.

    Google Scholar 

  56. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. International Conference on Database Theory, pp. 398–416, 1999.

    Google Scholar 

  57. J. Pei, and J. Han. Can we push more constraints into frequent pattern mining? ACM KDD Conference, 2000.

    Google Scholar 

  58. J. Pei, J. Han, R. Mao. CLOSET: An Efficient Algorithms for Mining Frequent Closed Itemsets, DMKD Workshop, 2000.

    Google Scholar 

  59. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-mine: Hyper-structure mining of frequent patterns in large databases. In Data Mining, ICDM Conference, 2001.

    Google Scholar 

  60. J. Pei, J. Han, and L. V. S. Lakshmanan. Mining Frequent Patterns with Convertible Constraints in Large Databases, ICDE Conference, 2001.

    Google Scholar 

  61. J. Pei, J. Han, and W. Wang. Constraint-based Sequential Pattern Mining: The Pattern-Growth Methods, Journal of Intelligent Information Systems, 28(2), pp. 133–160, 2007.

    Google Scholar 

  62. P. Shenoy, J. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, D. Shah. Turbo-charging Vertical Mining of Large Databases. ACM SIGMOD Conference, pp. 22–33, 2000.

    Google Scholar 

  63. J. Srivastava, R. Cooley, M. Deshpande, and P. N. Tan. Web usage mining: Discovery and applications of usage patterns from Web data. ACM SIGKDD Explorations Newsletter, 1(2), pp. 12–23, 2000.

    Article  Google Scholar 

  64. Y. Tong, L. Chen, Y. Cheng, P. Yu. Mining Frequent Itemsets over Uncertain Databases. PVLDB, 5(11), pp. 1650–1661, 2012.

    Google Scholar 

  65. V. S. Verykios, A. K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni. Association rule hiding. IEEE Transactions on Knowledge and Data Engineering, pp. 434–447, 16(4), pp. 434–447, 200–4.

    Google Scholar 

  66. J. Vreeken, M. van Leeuwen, and A. Siebes. Krimp: Mining itemsets that compress. Data Mining and Knowledge Discovery, 23(1), pp. 169–214, 2011.

    Article  MATH  MathSciNet  Google Scholar 

  67. J. Wang, J. Han, and J. Pei. CLOSET+: Searching for the Best strategies for mining frequent closed itemsets. ACM KDD Conference, 2003.

    Google Scholar 

  68. Z. Xing, J. Pei, and E. Keogh. A Brief Survey on Sequence Classification, ACM SIGKDD Explorations, 12(1), 201–0.

    Google Scholar 

  69. X. Yan, P. S. Yu, and J. Han, Graph indexing: A frequent structure-based approach. ACM SIGMOD Conference, 2004.

    Google Scholar 

  70. X. Yan, P. S. Yu, and J. Han. Substructure similarity search in graph databases. ACM SIGMOD Conference, 2005.

    Google Scholar 

  71. X. Yan, F. Zhu, J. Han, and P. S. Yu. Searching substructures with superimposed distance, ICDE Conference, 2006.

    Google Scholar 

  72. M. Zaki. Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE Transactions on Knowledge and Data Engineering, 17(8), pp. 1021–1035, 2005.

    Article  Google Scholar 

  73. M. Zaki, C. Aggarwal. XRules: An Effective Classifier for XML Data, ACM KDD Conference, 2003.

    Google Scholar 

  74. M. Zaki, C. J. Hsiao.: An Efficient Algorithm for Closed Frequent Itemset Mining, SDM Conference, 2002.

    Google Scholar 

  75. S. Zhang, T. Wang. Discovering Frequent Agreement Subtrees from Phylogenetic Data. IEEE Transactions on Knowledge and Data Engineering, 20(1), pp. 68–82, 2008.

    Article  Google Scholar 

  76. Z. Zou, J. Li, H. Gao, and S. Zhang. Mining Frequent Subgraph Patterns from Uncertain Graph Data, IEEE Transactions on Knowledge and Data Engineering, 22(9), pp. 1203–1218, 2010.

    Google Scholar 

  77. http://fimi.ua.ac.be/

Download references

Author information

Authors and Affiliations

  1. IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA

    Charu C. Aggarwal

Authors
  1. Charu C. Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Charu C. Aggarwal .

Editor information

Editors and Affiliations

  1. IBM, Yorktown Heights, New York, USA

    Charu C. Aggarwal

  2. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Jiawei Han

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Aggarwal, C. (2014). An Introduction to Frequent Pattern Mining. In: Aggarwal, C., Han, J. (eds) Frequent Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07821-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07820-5

  • Online ISBN: 978-3-319-07821-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation