Research on Clustering Method Based on Weighted Distance Density and K-Means
نویسندگان
چکیده
منابع مشابه
Weighted K-Means for Density-Biased Clustering
Clustering is a task of grouping data based on similarity. A popular k-means algorithm groups data by firstly assigning all data points to the closest clusters, then determining the cluster means. The algorithm repeats these two steps until it has converged. We propose a variation called weighted k-means to improve the clustering scalability. To speed up the clustering process, we develop the r...
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Rough K-means algorithm has shown that it can provides a reasonable set of lower and upper bounds for a given dataset. With the conceptions of the lower and upper approximate sets, rough k-means clustering and its emerging derivatives become valid algorithms in vague information clustering. However, the most available algorithms ignore the difference of the distances between data objects and cl...
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chapter one is devoted to a moderate discussion on preliminaries, according to our requirements. chapter two which is based on our work in (24) is devoted introducting weighted semigroups (s, w), and studying some famous function spaces on them, especially the relations between go (s, w) and other function speces are invesigated. in fact this chapter is a complement to (32). one of the main fea...
15 صفحه اولMini-model method based on k -means clustering
Mini-model method (MM-method) is an instance-based learning algorithm similarly as the k-nearest neighbor method, GRNN network or RBF network but its idea is different. MM operates only on data from the local neighborhood of a query. The paper presents new version of the MM-method which is based on k-means clustering algorithm. The domain of the model is calculated using k-means algorithm. Clus...
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ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2020
ISSN: 1877-0509
DOI: 10.1016/j.procs.2020.02.056