Clustering method

Abstract

The invention discloses a clustering method. The method comprises the steps that firstly, the pre-classification technology based on the density algorithm is used for obtaining a high-density core class, and a class hierarchy tree capable of representing a dataset structure is determined; then, K-MEANS clustering is carried out according to subclass centers with high representativeness in the class hierarchy tree to obtain fine clusters; finally, the fine clusters are combined according to class attributes in the class hierarchy tree to achieve a precise and stable clustering effect. The stable algorithm based on the fine clusters is provided according to sensibility of K-MEANS to initial clustering centers, convex type classes in a dataset can be divided, and the optimal division can be carried out on classes in irregular shapes.

Description

technical field

[0001] The invention relates to a clustering method, in particular to a novel K-MEANS clustering method, which belongs to the technical field of data mining. Background technique

[0002] With the development of the Internet, data has been shared and accumulated in large quantities, and the phenomenon of data overload and insufficient knowledge has become more and more prominent. The ever-expanding data will become a data grave because it is not utilized. If it can be fully tapped, the potential information contained in it will create a lot of value. The task of data mining is to discover knowledge from massive data. It is mainly aimed at structured data. In fact, a large amount of data is stored in the database in the form of text, which makes text data mining an important branch of data mining.

[0003] Clustering technology is a key means of data mining, and its task is to classify texts with similar subject content into one category, while separate texts...

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