A methodology to discover and understand complex patterns: Interpreted Integrative Multiview Clustering (I2MC)

•The proposed I2MC has some advantages regarding classical hierarchical methods.•I2MC methodology is suitable for understanding profiles of real data.•Proposed Cluster Interpretation methodology CI-IMS obtains automatic profiles.•The proposed Sensitivity Analysis with the generalized Test-Value allo...

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Bibliographic Details
Published in:Pattern recognition letters Vol. 93; pp. 85 - 94
Main Authors: Sevilla-Villanueva, Beatriz, Gibert, Karina, Sànchez-Marrè, Miquel
Format: Journal Article Publication
Language:English
Published: Amsterdam Elsevier B.V 01.07.2017
Elsevier Science Ltd
Elsevier
Subjects:
41 Approximations and expansions
65 Numerical analysis
65D Numerical approximation and computational geometry
Automatic profiling
Classificació AMS
Cluster Interpretation
Clustering
Complex variables
Complexity
Crossing clustering
Data analysis
Datasets
Geometria computacional
Matemàtiques i estadística
Methodology
Multiview Clustering
Nutrition
Nutrition research
Nutritional studies
Partitions
Àrees temàtiques de la UPC
Cluster Interpretation
Multiview Clustering
Crossing clustering
41A10
65D05
65D17
Nutritional studies
Automatic profiling
41A05
ISSN:0167-8655, 1872-7344
Online Access:Get full text
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Summary:•The proposed I2MC has some advantages regarding classical hierarchical methods.•I2MC methodology is suitable for understanding profiles of real data.•Proposed Cluster Interpretation methodology CI-IMS obtains automatic profiles.•The proposed Sensitivity Analysis with the generalized Test-Value allows graduate the robustness of the descriptors.•NCI-IMS deals with consistency between the interpretations of nested partitions. The main goal of this work is to develop a methodology for finding nutritional patterns from a variety of individual 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 a combination of advanced clustering techniques and consistent conceptual interpretation of clusters is proposed to find more understandable patterns or clusters. The Interpreted Integrative Multiview Clustering (I2MC) combines the previously proposed Integrative Multiview Clustering (IMC) with a new interpretation methodology NCIMS. IMC uses crossing operations over the several partitions obtained with the different views. Comparison with other classical clustering techniques is provided to assess the performance of this approach. IMC helps to reduce the high dimensionality of the data based on multiview division of variables. Two innovative Cluster Interpretation methodologies are proposed to support the understanding of the clusters. These are automatic methods to detect the significant variables that describe the clusters; also, a mechanism to deal with the consistency between the interpretations inter clusters of a single partition CI-IMS, or between pairs of nested partitions NCIMS. Some formal concepts are specifically introduced to be used in the NCIMS. I2MC is used to validate the interpretability of the participant’s profiles from an intervention nutritional study. The method has advantages to deal with complex datasets including heterogeneous variables corresponding to different topics and is able to provide meaningful partitions.
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ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2017.02.008