Name
Title | ||
|---|---|---|
Dealing with Distances and Transformations for Fuzzy C-Means Clustering of Compositional Data |
Abstract | ||
|---|---|---|
Clustering techniques are based upon a dissimilarity or distance measure between objects and clusters. This paper focuses on the simplex space, whose elements—compositions—are subject to non-negativity and constant-sum constraints. Any data analysis involving compositions should fulfill two main principles: scale invariance and subcompositional coherence. Among fuzzy clustering methods, the FCM algorithm is broadly applied in a variety of fields, but it is not well-behaved when dealing with compositions. Here, the adequacy of different dissimilarities in the simplex, together with the behavior of the common log-ratio transformations, is discussed in the basis of compositional principles. As a result, a well-founded strategy for FCM clustering of compositions is suggested. Theoretical findings are accompanied by numerical evidence, and a detailed account of our proposal is provided. Finally, a case study is illustrated using a nutritional data set known in the clustering literature. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s00357-012-9105-4 | J. Classification |
Keywords | Field | DocType |
clustering technique,data analysis,simplex space,clustering literature,common log-ratio transformation,compositional data,fcm algorithm,case study,nutritional data,fuzzy clustering method,fcm clustering,fuzzy clustering | Fuzzy clustering,Data mining,CURE data clustering algorithm,Clustering high-dimensional data,Correlation clustering,Consensus clustering,FLAME clustering,Statistics,Cluster analysis,Mathematics,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
29 | 2 | 0176-4268 |
Citations | PageRank | References |
2 | 0.45 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Javier Palarea-Albaladejo | 1 | 12 | 2.53 |
| Josep Antoni Martín-Fernández | 2 | 2 | 0.45 |
| Jesús A. Soto | 3 | 13 | 2.67 |