Density peak-based big data mining method and apparatus

Abstract

Embodiments of the invention disclose a density peak-based big data mining method and apparatus. The method comprises a first stage of selecting initial clustering centers by using a density peak clustering algorithm, and a second stage of determining an initial clustering number and accelerating convergence of a conventional fuzzy C-mean algorithm. In the algorithm of the first stage, a decision graph is provided by using density peak clustering, the initial clustering centers are selected, and the initial clustering center number is namely a clustering center number after the selection is finished, so that the clustering number does not need to be manually specified. In the algorithm of the second stage, an iterative process of the conventional fuzzy C-mean algorithm is optimized, the influence of local density on the algorithm is considered, and a density weighting factor is added, so that the algorithm can obtain a global optimal solution in an accelerated way; and an oscillating factor is added, so that the algorithm convergence can be accelerated.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a density peak-based big data mining method and device. Background technique [0002] Clustering is to divide all the objects in the sample space into several groups, so that the objects in the same group have a high similarity, while the objects in different groups have great differences. Among them, fuzzy C-means clustering, as a typical representative of fuzzy clustering, has a wide range of applications. However, the fuzzy C-means clustering algorithm has some disadvantages, mainly in being sensitive to the initial cluster center, easy to converge to local optimum, and slow in convergence speed. [0003] Density peak algorithm (Clustering by fast search and find of density peaks, CFSFDP) is a heuristic algorithm based on density clustering. Density peak clustering is based on the assumption that for a data set, the cluster center is surrounded by some data points with low local d...

AI Technical Summary

Problems solved by technology

However, the fuzzy C-means clustering algorithm has some shortcomings, mainly in being sensitive to the initial cluster center, easy to converge to local optimum, slow convergence speed, etc.

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