An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain
Download An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
An effective clustering method based on data indeterminacy in neutrosophic set domain
In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The main contribution is to use NS to handle boundary and outlier points as challenging points of clustering methods.
An effective clustering method based on data indeterminacy in neutrosophic set domain
In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The main contribution is to use NS to handle boundary and outlier points as challenging points of clustering methods. In the first step, a new de nition of data indeterminacy (indeterminacy set) is proposed in NS domain based on density properties of data.
Neutrosophic Clustering Algorithm Based on Sparse Regular Term Constraint
Clustering algorithm is one of the important research topics in the field of machine learning. Neutrosophic clustering is the generalization of fuzzy clustering and has been applied to many fields. this paper presents a new neutrosophic clustering algorithm with the help of regularization. Firstly, the regularization term is introduced into the FC-PFS algorithm to generate sparsity, which can reduce the complexity of the algorithm on large data sets. Secondly, we propose a method to simplify the process of determining regularization parameters. Finally, experiments show that the clustering results of this algorithm on artificial data sets and real data sets are mostly better than other clustering algorithms. Our clustering algorithm is effective in most cases.