Parameterization-free clustering algorithm and system based on minimum spanning tree

Abstract

The invention provides a parameterization-free clustering algorithm and system based on a minimum spanning tree, and the method comprises the following steps: firstly abstracting a to-be-clustered data set into an empowerment complete graph WCG, where points represent vectors, and empowerment edges represent the similarity relation between data; converting the WCG into a fully connected minimum spanning tree MST; clustering the one-dimensional weight space of the MST edge set by using a K-means algorithm in which k = 2 to obtain a pruning threshold; and finally, performing pruning and noise filtering on the MST, and obtaining a connected component being a clustered cluster. According to the algorithm, an original high-complexity uncertain-category-number dimensional space clustering problem is converted into a low-complexity two-category-number one-dimensional space clustering problem; according to the method, the defects of the K-means algorithm are overcome, non-parameterized clustering is truly realized, the clustering efficiency is improved while the clustering time is shortened, and the dependence of the algorithm on empirical parameters is eliminated.

Description

technical field [0001] The invention relates to a non-parameterized clustering algorithm based on the minimum spanning tree, which belongs to the field of clustering algorithms. Background technique [0002] Clustering algorithm is a very effective unsupervised machine learning algorithm, which is an important branch in the field of data mining. Traditional clustering algorithms can be roughly divided into partition clustering methods, hierarchical clustering methods, density clustering methods, grid clustering methods, model clustering methods and so on. As a partition clustering algorithm, K-means algorithm has many advantages such as simple principle, easy description, high time efficiency and suitable for processing large-scale data, so it is widely used in many fields. However, this algorithm also has obvious defects: the accuracy and computational complexity of clustering depend heavily on the selection of initial cluster number k and initial cluster center parameters...

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Problems solved by technology

However, this algorithm also has obvious defects: the accuracy and computational complexity of clustering are heavily dependent on the selection of initial cluster number k and initial cluster center parameters

In a large number of practical application scenarios, the data set is not only large in scale, but also in the process of dynamic change, so the number

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