Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning

TL;DR

This paper introduces a probabilistic spatial clustering algorithm based on the Self Discipline Learning model, which effectively handles high-dimensional data without predefining cluster numbers, achieving high accuracy and recall.

Contribution

The paper proposes a novel clustering algorithm that leverages the SDL model and Gaussian probability distribution to improve clustering performance on high-dimensional data.

Findings

Accuracy rate of 99.03% on traffic light dataset

Recall rate of 91% on traffic light dataset

Effective in reducing local optima issues in deep learning clustering

Abstract

Unsupervised clustering algorithm can effectively reduce the dimension of high-dimensional unlabeled data, thus reducing the time and space complexity of data processing. However, the traditional clustering algorithm needs to set the upper bound of the number of categories in advance, and the deep learning clustering algorithm will fall into the problem of local optimum. In order to solve these problems, a probabilistic spatial clustering algorithm based on the Self Discipline Learning(SDL) model is proposed. The algorithm is based on the Gaussian probability distribution of the probability space distance between vectors, and uses the probability scale and maximum probability value of the probability space distance as the distance measurement judgment, and then determines the category of each sample according to the distribution characteristics of the data set itself. The algorithm is tested in Laboratory for Intelligent and Safe Automobiles(LISA) traffic light data set, the accuracy rate is 99.03%, the recall rate is 91%, and the effect is achieved.