16 days ago | By Springer
Abstract
We propose in this paper a partial order framework for clustering incomplete data. The paramount feature of this framework is that it spans over a partial order that can be leveraged to establish data similarity. We present the underlying theoretical foundations and study the convergence of clustering algorithms in this framework. In addition, we present a partial order-based clustering algorithm that illustrates the embedding of K-means clustering algorithm in our framework. The first contribution of our method is that unlike methods based on imputation of the missing values, our...
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