We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Functional data clustering using K-means and random projection with applications to climatological data.
- Authors
Ashkartizabi, Mehdi; Aminghafari, Mina
- Abstract
In this paper, an efficient pattern recognition method for functional data is introduced. The proposed method works based on reproducing kernel Hilbert space (RKHS), random projection and K-means algorithm. First, the infinite dimensional data are projected onto RKHS, then they are projected iteratively onto some spaces with increasing dimension via random projection. K-means algorithm is applied to the projected data, and its solution is used to start K-means on the projected data in the next spaces. We implement the proposed algorithm on some simulated and climatological datasets and compare the obtained results with those achieved by K-means clustering using a single random projection and classical K-means. The proposed algorithm presents better results based on mean square distance (MSD) and Rand index as we have expected. Furthermore, a new kernel based on a wavelet function is used that gives a suitable reconstruction of curves, and the results are satisfactory.
- Subjects
PATTERN recognition systems; CLUSTER analysis (Statistics); RANDOM projection method; ATMOSPHERIC models; DATA mining; DATA visualization; KERNEL functions
- Publication
Stochastic Environmental Research & Risk Assessment, 2018, Vol 32, Issue 1, p83
- ISSN
1436-3240
- Publication type
Article
- DOI
10.1007/s00477-017-1441-9