Abstract
Searching information in a huge amount of data can be a difficult task. To support this task several strategies are used. Classification of data and labeling are two of these strategies. Used separately each of these strategies have certain limitations. Algorithms used to support the process of automated classification influence the result. In addition, many noisy classes can be generated. On the other hand, labeling of document can help recall but it can be time consuming to find metadata. This paper presents a method that exploits the notion of association rules and maximal association rules, in order to assist textual data processing, these two strategies are combined.
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References
Bertin-Mahieux, T., Eck, D., Mandel, M.: Automatic Tagging of Audio: The State-of-the-Art. In: Wang, W. (ed.) Machine Audition: Principles, Algorithms and Systems. IGI Publishing (2010)
Estes, W.K.: Classification and Cognition. Oxford University Press (1994) ISBN 0-19-510974-0
Agrawal, R., Imielinski, T., Swami, A.: Minning association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 207–216 (1993)
Le Bras, Y., Meyer, P., Lenca, P., Lallich, S.: Mesure de la robustesse de règles d’association. In: Proceedings of the QDC 2010, Hammamet, Tunisie (2010)
Vaillant, B.: Mesurer la qualité des règles d’association : études formelles et expérimentales, Thesis École Nationale Supérieure des Télécommunications of Bretagne (2006)
Agrawal, A., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Poceedings of the 20th International Conference on Very Large Database, pp. 487–499 (1994)
Amir, A., Aumann, Y., Feldman, R., Fresko, M.: Maximal Association Rules: A Tool for Mining Associations in Text. Journal of Intelligent Information System 25(3), 333–345 (2005)
Biskri, I., Rompré, L., Descoteaux, S., Achouri, A., Amar Bensaber, B.: Extraction of Strong Associations in Classes of Similarities. In: Proceedings of IEEE/ICMLA, Boca Raton, Florida, USA (2012)
Biskri, I., Hilali, H., Rompré, L.: Extraction de relations d’association maximales dans les textes. Actes du JADT, 173–182 (2010)
Miller, E., Shen, D., Liu, J., Nicholas, C., Chen, T.: Techniques for Gigabyte-Scale N-gram Based Information Retrieval on Personal Computers. In: Proceedings of the PDPTA 1999, Las Vegas, U.S.A. (1999)
Damashek, M.: Gauging Similarity with n-Grams: Language-Independent Categorization of Text. Science 267, 843–848 (1995)
Carpenter, G., Grossberg, S.: Fuzzy ART: Fast Stable Learning and Categorisation of Analog Patterns by an Adaptative Resonance System. Neural Network 4, 759–771 (1991)
MacQueen, J.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press (1967)
Kohonen, T.: The Self-Organisation Map. Proceedings of the IEEE 78(9), 1464–1480 (1990)
Anderson, J.: An Introduction to Neural Network. MIT Press (1995) ISBN 0-262-01144-1
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company (1994) ISBN 0-02-352761-7
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Biskri, I., Rompré, L., Jouis, C., Achouri, A., Descoteaux, S., Bensaber, B.A. (2013). Seeking for High Level Lexical Association in Texts. In: Amine, A., Otmane, A., Bellatreche, L. (eds) Modeling Approaches and Algorithms for Advanced Computer Applications. Studies in Computational Intelligence, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-00560-7_8
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DOI: https://doi.org/10.1007/978-3-319-00560-7_8
Publisher Name: Springer, Cham
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