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Given the increasing demand for blood smear analysis in the Hematology department of Oran (Algeria) Hospital and worldwide, in the literature there are some methods directed to the automation of this important problem. This paper presents a state-of-art about the used clustering methods.

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Purpose This study aims to evaluate a new fusion technique of visual and textual clusters of objects from a real multimedia data-driven collection to improve the performance of multimedia applications. Design/methodology/approach The authors focused on using multi-criteria for clustering texts and images. The algorithm consists of these steps: first is text representation using the statistical method of weighting, second is image representation using a bag of words feature descriptors methods and finally application of multi-criteria clustering. Findings As an application for event detection based on social multimedia data, in particular, Flickr platform. Several experiments were conducted to choose the appropriate parameters for a better scheme of clustering. The new approach achieves better performance when aggregate text clustering is done with image clustering for event detection. Research limitations/implications Further researches would be investigated on other social media platforms such as Facebook and Twitter for a generalization of the technique. Originality/value This study contributes to multimedia data mining through the new fusion technique of clustering. The technique has its root in such strong field as the field of multi-criteria clustering and decision-making support.

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