Feedback density peak value clustering method and system thereof

Abstract

The invention provides a feedback density peak value clustering method and a system thereof. A problem that one type is divided into a plurality of types when a plurality of density peak values are generated in one type in an original density peak value algorithm is solved. Simultaneously, accuracy of an original algorithm in a high dimension data set is increased. The method comprises the following steps of 1, using non-negative matrix factorization to carry out characteristic extraction on a data set; 2, according to an original density peak value clustering algorithm, drawing a decision graph and selecting a plurality of clustering centers; 3, using a ''nearest neighbor'' algorithm to distribute the rest of points and removing a noise point; 4, using a SVM to feed back a clustering result between two types; and 5, according to a feedback result, merging the types which can be merged. By using the method, robustness of a density peak value algorithm can be effectively increased, clusters with any shapes can be well discovered, high dimension data can be effectively processed, and a good clustering effect is possessed.

Description

technical field [0001] The present invention is a feedback density peak clustering method and system, which can automatically cluster on a data set of any shape, and relates to the fields of pattern recognition and machine learning. In particular, it involves using the SVM model to feed back the clustering results between two classes, and designing a new feedback strategy to merge the classes according to the support vector obtained from the SVM training to obtain accurate clustering results. Background technique [0002] Clustering analysis is unsupervised learning and is an important research direction of data mining. It can be roughly divided into five types of clustering algorithms: partition-based, hierarchy-based, model-based, density-based and grid-based. The density-based clustering algorithm regards clusters as high-density object regions separated by low-density regions in the data space, and the shape of the clusters has no benchmark, which can be used to filter o...

AI Technical Summary

Problems solved by technology

However, when multiple density peaks appear in a class during clustering, abnormal sample points will be selected as pseudo-clustering centers, and a class will be divided into multiple classes, which will affect the clustering results. Not very accurate on dimensional datasets

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