Skip to main content
SpringerLink
Log in

An Efficient Way to Find Frequent Patterns Using Graph Mining and Network Analysis Techniques on United States Airports Network

  • Conference paper
  • First Online:
Book cover Smart Computing and Informatics

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 78))

Abstract

We are currently in the Information Age where massive amounts of data is being collected and analyzed to find interesting and frequent patterns. The need for mining data has been steadily increasing over the past few years. Graphs are one of the best studied data structures in the fields of mathematics and computer science. And due to this, in the recent years graph-based data mining has become quite popular. Graph data mining uses the graph nodes and the links between them to represent the entities, their relationships with other entities and their attributes and discovers interesting patterns in the graphs. Transportation networks are networks of routes from one location to another through various modes of travel. In this article, we use a transportation network of airports in United States of America and apply graph data mining techniques and network analysis techniques on US airports and flights datasets.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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. Cook, D. J., & Holder, L. B. (Eds.). (2006). Mining graph data. John Wiley & Sons.

    Google Scholar 

  2. Sabitha, A. S., Mehrotra, D., & Bansal, A. (2012, May). Quality metrics a quanta for retrieving learning object by clustering techniques. In Digital information and communication technology and it’s applications (DICTAP), 2012 Second International Conference on (pp. 428–433). IEEE.

    Google Scholar 

  3. Sabitha, A. S., Mehrotra, D., & Bansal, A. (2016). Delivery of learning knowledge objects using fuzzy clustering. Education and Information Technologies, 21(5), 1329–1349.

    Google Scholar 

  4. Livne, A., Adar, E., Teevan, J., & Dumais, S. (2013, February). Predicting citation counts using text and graph mining. In Proc. the iConference 2013 Workshop on Computational Scientometrics: Theory and Applications.

    Google Scholar 

  5. Yan, X., & Han, J. (2002). gspan: Graph-based substructure pattern mining. In Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on (pp. 721–724). IEEE.

    Google Scholar 

  6. Gudes, E., Shimony, S. E., & Vanetik, N. (2006). Discovering frequent graph patterns using disjoint paths. IEEE Transactions on Knowledge and Data Engineering, 18(11), 1441–1456.

    Google Scholar 

  7. Chen, C. C., Lee, K. W., Chang, C. C., Yang, D. N., & Chen, M. S. (2013, October). Efficient large graph pattern mining for big data in the cloud. In Big Data, 2013 IEEE International Conference on (pp. 531–536). IEEE

    Google Scholar 

  8. Tanupriya Choudhury, Vivek Kumar and Darshika Nigam, Cancer Research Through The Help of Soft Computing Techniques: A Survey, International Journal of Computer Science and Mobile Computing, IJCSMC, vol. 2, issue 4, pg. 467–477, April (2013)

    Google Scholar 

  9. Getoor, L. (2003). Link mining: a new data mining challenge. ACM SIGKDD Explorations Newsletter, 5(1), 84–89.

    Google Scholar 

  10. Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. Acm Sigkdd Explorations Newsletter, 1(2), 12–23.

    Google Scholar 

  11. Patel, S. J., & Pattewar, T. M. (2014, July). Software birthmark based theft detection of JavaScript programs using agglomerative clustering and Frequent Subgraph Mining. In Embedded Systems (ICES), 2014 International Conference on (pp. 63–68). IEEE.

    Google Scholar 

  12. King, R. D., Srinivasan, A., & Dehaspe, L. (2001). Warmr: a data mining tool for chemical data. Journal of Computer-Aided Molecular Design, 15(2), 173–181.

    Google Scholar 

  13. Ketkar, N. S., Holder, L. B., & Cook, D. J. (2005, August). Subdue: Compression-based frequent pattern discovery in graph data. In Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations (pp. 71–76). ACM.

    Google Scholar 

  14. Inokuchi, A., Washio, T., & Motoda, H. (2000, September). An apriori-based algorithm for mining frequent substructures from graph data. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 13–23). Springer Berlin Heidelberg.

    Google Scholar 

  15. Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487–499).

    Google Scholar 

  16. Bureau of Transportation Statistics database (n.d.) Retrieved from http://www.transtats.bts.gov/DataIndex.asp

  17. Yan, X., Yu, P. S., & Han, J. (2004, June). Graph indexing: a frequent structure-based approach. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data (pp. 335–346). ACM.

    Google Scholar 

  18. Wang, W., Wang, C., Zhu, Y., Shi, B., Pei, J., Yan, X., & Han, J. (2005, June). Graphminer: a structural pattern-mining system for large disk-based graph databases and its applications. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data (pp. 879–881). ACM.

    Google Scholar 

  19. Palmer, C. R., Gibbons, P. B., & Faloutsos, C. (2002, July). ANF: A fast and scalable tool for data mining in massive graphs. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 81–90). ACM.

    Google Scholar 

  20. Kuramochi, M., & Karypis, G. (2004). An efficient algorithm for discovering frequent subgraphs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1038–1051.

    Google Scholar 

  21. Huan, J., Wang, W., Prins, J., & Yang, J. (2004, August). Spin: mining maximal frequent subgraphs from graph databases. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 581–586). ACM.

    Google Scholar 

  22. Meinl, T., Borgelt, C., & Berthold, M. (2004). Discriminative closed fragment mining and perfect extensions in MoFa (pp. 3–14).

    Google Scholar 

  23. Williams, M., Burry, J., & Rao, A. (2015, March). Graph mining indoor tracking data for social interaction analysis. In Pervasive Computing and Communication Workshops (PerCom Workshops), 2015 IEEE International Conference on (pp. 2–7). IEEE.

    Google Scholar 

  24. Steinbauer, M., & Kotsis, G. (2013, June). Platform for General-Purpose Distributed Data-Mining on Large Dynamic Graphs. In Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2013 IEEE 22nd International Workshop on (pp. 178–183). IEEE.

    Google Scholar 

  25. Nettleton, D. F. (2013). Data mining of social networks represented as graphs. Computer Science Review, 7, 1–34.

    Google Scholar 

  26. Pinheiro, F., Kuo, M. H., Thomo, A., & Barnett, J. (2013, June). Extracting association rules from liver cancer data using the FP-growth algorithm. In Computational Advances in Bio and Medical Sciences (ICCABS), 2013 IEEE 3rd International Conference on (pp. 1–1). IEEE.

    Google Scholar 

  27. Sidhu, S., Meena, U. K., Nawani, A., Gupta, H., & Thakur, N. (2014). FP Growth Algorithm Implementation. International Journal of Computer Applications, 93(8).

    Google Scholar 

  28. Jia, Y., Zhang, J., & Huan, J. (2011). An efficient graph-mining method for complicated and noisy data with real-world applications. Knowledge and Information Systems, 28(2), 423–447.

    Google Scholar 

  29. Akoglu, L., & Faloutsos, C. (2013, February). Anomaly, event, and fraud detection in large network datasets. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 773–774). ACM.

    Google Scholar 

  30. Hu, X. (2011, November). Data mining and its applications in bioinformatics: Techniques and methods. In Granular Computing (GrC), 2011 IEEE International Conference on (pp. 3–3). IEEE.

    Google Scholar 

  31. Xie, B., Kumar, A., Ramaswamy, P., Yang, L. T., & Agrawal, S. (2009, July). Social behavior association and influence in social networks. In Ubiquitous, Autonomic and Trusted Computing, 2009. UIC-ATC’09. Symposia and Workshops on (pp. 434–439). IEEE.

    Google Scholar 

  32. Ranjan, P., & Vaish, A. (2014, November). Apriori Viterbi Model for Prior Detection of Socio-Technical Attacks in a Social Network. In Engineering and Telecommunication (EnT), 2014 International Conference on (pp. 97–101). IEEE.

    Google Scholar 

  33. Peng, J. Y., Yang, L. M., Wang, J. X., Liu, Z., & Li, M. (2008, May). An efficient algorithm for detecting closed frequent subgraphs in biological networks. In 2008 International Conference on Bio Medical Engineering and Informatics (Vol. 1, pp. 677–681). IEEE.

    Google Scholar 

  34. Nawaz, W., Khan, K. U., & Lee, Y. K. (2014, December). Core analysis for efficient shortest path traversal queries in social graphs. In Big Data and Cloud Computing (BdCloud), 2014 IEEE Fourth International Conference on (pp. 363–370). IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India

    Anant Joshi, Abhay Bansal, A. Sai Sabitha & Tanupriya Choudhury

Authors
  1. Anant Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhay Bansal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Sai Sabitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tanupriya Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anant Joshi .

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Joshi, A., Bansal, A., Sai Sabitha, A., Choudhury, T. (2018). An Efficient Way to Find Frequent Patterns Using Graph Mining and Network Analysis Techniques on United States Airports Network. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 78. Springer, Singapore. https://doi.org/10.1007/978-981-10-5547-8_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-5547-8_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5546-1

  • Online ISBN: 978-981-10-5547-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation