Iteration text clustering method based on self-adaptation subspace study

Abstract

The invention discloses an iteration text clustering method based on self-adaptation subspace study. The method includes the following steps: (1) initiation: text linguistic data is expressed as a text vector space, initial K clusters are generated through an affine propagation clustering method, and all text clustering categories are expressed as an initial category affiliation indication matrix; and (2) iteration between the subspace projection and the clusters: the initial category affiliation indication matrix is used as prior knowledge, a maximum average neighborhood edge is used as a target to solve a subspace projection matrix, the text vector space is projected to a subspace, K clusters are generated through the affine propagation clustering method in the subspace, and a category affiliation indication matrix is updated; and a convergent function is calculated based on the subspace projection matrix and the category affiliation indication matrix till the function is converged, iteration exits, and text clustering is finished. The iteration text clustering method does not limit the capacity and distribution of text data, subspace solution and clusters are fused under a uniform frame, and an overall optimal clustering result is obtained through an iteration strategy.

Description

technical field [0001] The present invention relates to the field of machine learning and pattern recognition, in particular to an iterative text clustering method based on adaptive subspace learning, which is an adaptive subspace learning method based on the maximization of the average neighborhood edge, and adopts an iterative strategy Use it to solve text clustering problems. Background technique [0002] With the popularization and development of Internet technology and database technology, people can easily acquire and store large amounts of data. Most of the data in reality exists in the form of text. As a means, text clustering can organize, summarize and navigate text information, and help to accurately obtain the required information from the vast text information resources. Therefore, in recent years, it has been Gain widespread attention. [0003] In text clustering, text is often represented by Vector Space Model (VSM), but this representation is characterized ...

AI Technical Summary

Problems solved by technology

However, the limitation of NAML is that its optimization process must depend on multiple key parameters, which can easily lead to overfitting when the data is insufficient.

[0008] Although the idea of ​​adaptive dimensionality reduction and related methods can solve specific text clustering problems, there are also some technical defects pointed out above, which limit its application scope and leave a certain space for the improvement of text clustering algorithms.

Images