Abstract
Clustering is a hot research field in data mining. There are so many methods or algorithms designed for different type data set on which data analysis action operates. Local Agglomerative Characteristic (LAC) based Algorithm, in this paper, is presented for data clustering, which can handle clusters of different size, shapes, and densities, can work well on different distributed and natural variant data set. The new algorithm design based on the survey of the Jarvis-Patrick and the Shared Nearest Neighbor (SNN) density-based clustering algorithm, then can deal with these kind clusters and avoid theirs limitations in some extent, lead to improved clustering result.
Published Version
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