article · International Journal of Data Mining Modelling and Management · 2018

Interval graph mining

Amina Kemmar, Yahia Lebbah, Samir Loudni

Cited by 0Open access

Abstract

Frequent subgraph mining is a difficult data mining problem aiming to find the exact set of frequent subgraphs into a database of graphs. Current subgraph mining approaches make use of the canonical encoding which is one of the key operations. It is well known that canonical encodings have an exponential time complexity. Consequently, mining all frequent patterns for large and dense graphs is computationally expensive. In this paper, we propose an interval approach to handle canonicity, leading to two encodings, lower and upper encodings, with a polynomial time complexity, allowing to tightly enclose the exact set of frequent subgraphs. These two encodings lead to an interval graph mining algorithm where two minings are launched in parallel, a lower mining (resp. upper mining) using the lower (resp. upper) encoding. The interval graph mining approach has been implemented within the state of the art Gaston miner. Experiments performed on synthetic and real graph databases coming from stock market and biological datasets show that our interval graph mining is effective on dense graphs.

Cite this paper

APA 7

Kemmar, A., Lebbah, Y., & Loudni, S. (2018). Interval graph mining. International Journal of Data Mining Modelling and Management, 10(1), 1. https://doi.org/10.1504/ijdmmm.2018.089629

MLA 9

Kemmar, Amina, et al. “Interval graph mining.” International Journal of Data Mining Modelling and Management, vol. 10, no. 1, 2018, p. 1, https://doi.org/10.1504/ijdmmm.2018.089629.

Chicago

Kemmar, Amina, Yahia Lebbah, and Samir Loudni. 2018. “Interval graph mining.” International Journal of Data Mining Modelling and Management 10 (1): 1. https://doi.org/10.1504/ijdmmm.2018.089629.

Harvard

Kemmar, A., Lebbah, Y. and Loudni, S. (2018) ‘Interval graph mining’, International Journal of Data Mining Modelling and Management, 10(1), p. 1. Available at: https://doi.org/10.1504/ijdmmm.2018.089629.

BibTeX

@article{kemmar2018interval,
  title = {{Interval graph mining}},
  author = {Kemmar, Amina and Lebbah, Yahia and Loudni, Samir},
  journal = {International Journal of Data Mining Modelling and Management},
  volume = {10},
  number = {1},
  pages = {1},
  year = {2018},
  doi = {10.1504/ijdmmm.2018.089629}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.