RT Journal Article
T1 Band depth based initialization of K-means for functional data clustering
A1 Albert Smet, Javier
A1 Torrente Orihuela, Ester Aurora
A1 Romo, Juan
AB The k-Means algorithm is one of the most popular choices for clustering data but is well-known to be sensitive to the initialization process. There is a substantial number of methods that aim at finding optimal initial seeds for k-Means, though none of them is universally valid. This paper presents an extension to longitudinal data of one of such methods, the BRIk algorithm, that relies on clustering a set of centroids derived from bootstrap replicates of the data and on the use of the versatile Modified Band Depth. In our approach we improve the BRIk method by adding a step where we fit appropriate B-splines to our observations and a resampling process that allows computational feasibility and handling issues such as noise or missing data. We have derived two techniques for providing suitable initial seeds, each of them stressing respectively the multivariate or the functional nature of the data. Our results with simulated and real data sets indicate that our Functional Data Approach to the BRIK method (FABRIk) and our Functional Data Extension of the BRIK method (FDEBRIk) are more effective than previous proposals at providing seeds to initialize k-Means in terms of clustering recovery.
PB Springer
SN 1862-5347
YR 2023
FD 2023-06
LK https://hdl.handle.net/10016/37332
UL https://hdl.handle.net/10016/37332
LA eng
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the Spanish Ministry of Education [collaboration grant in university departments, Archive ID 18C01/003730] and the Spanish Ministry of Science, Innovation and Universities [grants numbers PID2020-116567GB-C22 and PID2020-112796RB-C22].
DS e-Archivo
RD 27 jul. 2024