Abstract
Clustering is by far the most commonly used unsupervised data mining techniques for discovering interesting knowledge and patterns. It aims to group a set of data objects into clusters that are coherent internally but basically different from each other. In this work, we involve clustering algorithms in Information Retrieval (IR) to strengthen the user’s original query with appropriate additional terms and return more relevant information. The overall procedure consists of the following steps: (i) use the k-medoids clustering algorithm to group terms into clusters with similar characteristics, (ii) involve k-means algorithm to calculate the centroid of query terms, (iii) select the relevant clusters to the original query and return the expansion term candidates, (iv) evaluate the expansion term candidates to the centroid and add the best ones to the original query, (v) run a search with the expanded query. We present numerical experiments based on real data from a large online health database. The results of our numerical testing demonstrate the effectiveness of the proposed method compared to prior state-of-the-art.
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Khennak, I., Drias, H., Kechid, A., Moulai, H. (2019). Clustering Algorithms for Query Expansion Based Information Retrieval. 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_23
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