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Unsupervised clustering algorithm can effectively reduce the dimension ofhigh-dimensional unlabeled data, thus reducing the time and space complexity ofdata processing. However, the traditional clustering algorithm needs to set theupper bound of the number of categories in advance, and the deep learningclustering algorithm will fall into the problem of local optimum. In order tosolve these problems, a probabilistic spatial clustering algorithm based on theSelf Discipline Learning(SDL) model is proposed. The algorithm is based on theGaussian probability distribution of the probability space distance betweenvectors, and uses the probability scale and maximum probability value of theprobability space distance as the distance measurement judgment, and thendetermines the category of each sample according to the distributioncharacteristics of the data set itself. The algorithm is tested in Laboratoryfor Intelligent and Safe Automobiles(LISA) traffic light data set, the accuracyrate is 99.03%, the recall rate is 91%, and the effect is achieved.

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