Xiao Zhang
ABSTRACT
Cluster analysis is one of the hot issues in the field of data mining and it has extensive applications in many aspects. The label propagation algorithm is easy to implement. At the same time, it has a low time complexity which has been recognized by scholars. Because the algorithm needs to specify the category labels of the data set, the accuracy and adaptability of the algorithm are affected. In view of the above problems, this paper proposes a new clustering algorithm that combines the advantages of density-based and label propagation. The algorithm adaptively determines the label of the data points through local density and reducing the effect of noise on the results. Experimental results show that the proposed algorithm has better adaptability while improving the accuracy of clustering results.
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Index Terms
- A Label Propagation Algorithm Based on Local Density of Data Points
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