In recent years, functional data has been widely
used in finance, medicine, biology and other fields. The current clustering
analysis can solve the problems in finite-dimensional space, but it is
difficult to be directly used for the clustering of functional data. In this
paper, we propose a new unsupervised clustering algorithm based on adaptive
weights. In the absence of initialization parameter, we use entropy-type
penalty terms and fuzzy partition matrix to find the optimal number of
clusters. At the same time, we introduce a measure based on adaptive weights to
reflect the difference in information content between different clustering
metrics. Simulation experiments show that the proposed algorithm has higher
purity than some algorithms.
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