Algorithms for clustering data
Algorithms for clustering data
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Facial expression recognition: a clustering-based approach
Pattern Recognition Letters
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Bayesian hierarchical clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unsupervised possibilistic clustering
Pattern Recognition
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
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The aim of this paper is to find feature-patterns related to the autonomy-disability level of elderly people living in nursing homes. These levels correspond to profiles based on the people's ability to perform activities of daily living like being able to wash, dress and move. To achieve this aim, an unsupervised approach is used. In this article, we propose a new clustering approach based on principal component analysis (PCA) to better approximate clusters. We want to automatically find categories or groups of residents based on their degree of autonomy-disability. All residents in a group have similar patterns. The main function of PCA is to explore the links between variables and the similarities between examples (individuals). The proposed algorithm uses the PCA technique to direct the determination of the clusters with self-organizing partitions by using the Euclidian distance. The study was carried out in close collaboration with the French cooperative health organization called the ''Mutualite Francaise de la Loire''. The quantitative data arises from the databases of four different nursing homes located in the city of Saint-Etienne in France. The study concerns 2271 observations of dependence evaluations corresponding to 628 residents.