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Distributed Kernel k-Means Clustering by stiva jobs is a document available to read on EtoBox.
This paper presents a distributed framework for the Kernel k-Means clustering algorithm to enhance its applicability to large datasets. By utilizing the MapReduce programming model and a novel kernel matrix trimming method, the proposed approach reduces memory requirements and improves clustering performance. The framework is evaluated through experiments focused on image clustering, demonstrating its effectiveness in handling big data challenges.
- Author
- stiva jobs
- Language
- EN