Identifying nutritional patterns through integrative multiview clustering
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The main goal of this work is to develop a methodology for finding nutritional patterns based on a variety of subject characteristics which can contribute to better understand the interactions between nutrition and health, provided that the complexity of the phenomenon gives poor performance using classical approaches. An innovative methodology based on advanced clustering techniques is proposed in order to find more compact patterns or clusters. The Integrative Multiview Clustering (IMC) combines Multiview Clustering approach with crossing operations over the several partitions obtained. Comparison with other classical clustering techniques is provided to assess the performance of our approach. The Dunn-like cluster validity index proposed by Bezdek & Pal is used for the comparison from a structural point of view, as it is more robust than the original Dunn index. The performance of the IMC method is better than other popular clustering techniques based on the Dunn-like Index. Our findings suggest that the Integrative Multiview Clustering provides more compact and separated clusters. In addition, IMC helps to reduce the high dimensionality of the data based on multiview division of attributes and also, the resulting partition is easier to interpret. Using the Integrative Multiview Clustering approach, a good partition is obtained from a structural point of view. Also, the interpretation of the resulting partition is clearer than the one obtained by classical approache
CitationSevilla-Villanueva, Beatriz, Gibert, Karina, Sanchez, M. Identifying nutritional patterns through integrative multiview clustering. A: "Frontiers in artificial intelligence and applications: artificial intelligence research and development". Amsterdam: IOSPress, 2015, p. 185-194.
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