Abstract
The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. the process of extraction of association rules is based on the lexical quality of the text and on the minimum support set by the user. In this paper, we propose to use frequent itemsets as descriptors and classifying them by using K-Medoids algorithm and Hierarchical cluster. We present how they can be identified and used to define a level of similarity between several segments. The experiments conducted demonstrate the potential of the proposed approach for defining similarity between segments.
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Bokhabrine, A., Biskri, I., Ghazzali, N. (2019). Frequent Itemsets as Descriptors of Textual Records. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_4
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DOI: https://doi.org/10.1007/978-3-030-28374-2_4
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