Abstract
The existing grey relational clustering method has limitations in the application of multidimensional sequences and cannot directly calculate the grey correlation degree between unequal-length sequences. In this paper, by introducing the multidimensional dynamic DTW distance into the existing 3D grey relational model, a new grey relational analysis model that can be applied to multidimensional data is proposed, which is based on DTW distance. The model does not require one-to-one correspondence of data points but evaluates the similarity of its geometric curves by calculating the shortest distance between sequences. In addition, since the traditional grey correlation clustering method is implemented, the method first extracts the reference sequence from the observation sequence and then calculates the similarity between the observation objects by calculating the grey correlation degree between each sequence and the reference sequence, so each object only needs to be calculated once. The experimental results show that the multidimensional grey correlation degree based on DTW distance and the grey relational clustering model oriented to multidimensional data are more accurate than other existing methods. Finally, the grey relation clustering method of multidimensional data is used to analyze the multiobjective human resource grey relational clustering model under time constraints, and the validity of the model is verified.
Highlights
Clustering, as an important data analysis method, has attracted the extensive attention of many scholars and is a research hotspot in machine learning, statistics, computer science, and other fields
For those data with a small sample size, insufficient information, and unclear sample rules, these methods often cannot get accurate results. e main object of grey relational clustering is such data [2]. It measures the similarity of the observation systems by a grey relational analysis model and simplifies the complex system by dividing similar objects into the same class
Linear interpolation is usually used to transform the observed data of the discrete behavior of the observation system into piecewise continuous lines, and a corresponding model is constructed according to the geometric characteristics of the lines to judge the similarity between the sequences, including the characteristics of distance, area, and slope [5]. e more similar objects are observed, the more similar their geometric characteristics are
Summary
Clustering, as an important data analysis method, has attracted the extensive attention of many scholars and is a research hotspot in machine learning, statistics, computer science, and other fields. Most of the existing clustering methods are aimed at the data with massive information [1] For those data with a small sample size, insufficient information, and unclear sample rules, these methods often cannot get accurate results. Some methods have the problem of the small correlation between indexes in the class [10] Aiming at these problems, some scholars put forward a grey index correlation clustering model [11]. E clustering rules of panel data can effectively avoid clustering sequences with small association degree into a group and can deal with unequal data This method is easy to be affected by dimensionality reduction results, and the introduction of new uncertainties will affect the final clustering accuracy [13]
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