A Fuzzy Clustering Method Based on Sparse Mean

Abstract

The present invention relates to a fuzzy clustering method based on the sparse mean value, expressing the documents to be clustered as high-dimensional sparse vectors with a vector space model, setting parameters, initializing the mean value, updating the values ​​of all membership degrees based on the current mean value, updating the weights, Then update the corresponding mean value based on the degree of membership. When the corresponding mean value does not change or the number of iterations is the largest, the iteration ends and the clustering result is output, otherwise repeat. The present invention uses the sparse mean value to make the mean value, that is, the class center point have the same local sparse characteristics as the sample point, and increase the effectiveness of describing the sample point and class similarity based on the Euclidean distance between the sample point and the mean value, which is more efficient in time and generates The mean value with sparse characteristics makes the center point of the class more naturally represent the characteristics of sparse sample points. At the same time, in order to increase the control of the sparsity of the mean value, a regular term of the mean norm is added to the objective function to obtain a new minimum objective function , allowing for a faster solution.

Description

technical field [0001] The invention belongs to the technical field of calculation, estimation and counting, and in particular relates to a fuzzy clustering method based on sparse mean designed for high-dimensional sparse data. Background technique [0002] In many practical problems in many fields, it is necessary to use effective clustering methods to group objects in high-dimensional sparse data sets to analyze the internal structure of the data and mine useful knowledge to help people make further decisions, such as grouping news documents to detect them. topics included. [0003] Fuzzy clustering analysis is an analysis method for clustering objective things by establishing a fuzzy similarity relationship based on the characteristics, closeness, and similarity between objective things. Its advantage over hard clustering is the introduction of fuzzy membership by means of fuzzy set theory. concept, which can naturally describe the overlap between classes. [0004] Howe...

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Problems solved by technology

[0005] The technical problem solved by the present invention is that in the prior art, the number of samples required for correct estimation of the potential probability distribution in the vector space will grow exponentially with the increase of the dimension, and at the same time, the traditional fuzzy k-means algorithm is based on the Euclidean distance To measure the distance from the sample point to the class center point, without any constraints, the mean of high-dimensional sparse data is not sparse, and the resulting traditional fuzzy clustering is not very good for high-dimensional data such as text data. , the Euclidean distance between the sample point (high-dimensional sparse vector) and the mean (high-dimensional non-sparse vector) cannot effectively measure the similarity between the sample point and the class, and then provides an optimized sparse mean-based fuzzy aggregation class method

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