A partial order framework for incomplete data clustering
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 method does not make any assumptions about missing data. Another important contribution is that it alleviates false dismissals caused by other interval-based similarity measures. The experimental results show that, although our method do not assume any prior knowledge of missing data, it is competitive to most of published incomplete data clustering methods that are based on assumptions...
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Hilaris Conferences is going to host its premier World Biotechnology Congress 2019 during October 01-02, 2019, Valencia, Spain. Biotechnology 2019 Congress aim to create a... Read more ...
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