Research On Multi-granularity Decomposition Mechanism Of Complex Tasks Based On Density Peaks

The big data intelligent mining is an important direction in the current research and application development of big data mining.The complexity of the task is the key challenge,so it is of great significance to explore the decomposition and solution of complex tasks.At present,solving methods of complex tasks are mainly based on supervised or semi-supervised methods and plenty of learning is required before decomposition and solution.However,in real situations,it is difficult to obtain a large amount of prior knowledge for learning sometimes.Therefore,this paper simulates the multi-granularity observation and analysis of human thinking,adopts the information granulation strategy of density peaks clustering,and proposes a decomposition and solution mechanism of complex tasks based on granular computing theory,so as to explore the application effect of multi-granularity decomposition and solution mechanism in practical complex problems.Main contributions of this paper are as follows:1.A multi-granularity decomposition and solution method for complex networks based on density peaks is proposed.This method mainly explores the effect of multi-granularity decomposition and solution mechanism based on density peaks on solving problems discovered by community in complex networks.Firstly,the density peaks algorithm is used to form the global task guidance tree for task solution,and the measure formula and threshold of similarity are defined to determine whether the subtask sets can be decomposed from the global task.Secondly,in the discovery task solving model of the multi-granularity community in complex networks,solving space of the biggest coarse-grained task is obtained by using the complex task decomposition algorithm based on the density peaks.According to the granulation rules,we can granulate the solving space of the coarsest grain layer to several solving spaces of finer grain layers.Finally,the effectiveness of this method is proved by a comparative experiment.It is proved that this method can quickly and automatically discover the hierarchical relationship of community structure in social networks,and can effectively discover the multi-granularity community.2.A multi-granularity decomposition and solution method for complex images based on density peaks is proposed.This method mainly explores the application of multi-granularity decomposition mechanism in image segmentation based on density peaks.Firstly,a multi-granularity Gaussian cloud transform algorithm based on the density peaks is presented.By improving Gaussian cloud transform algorithm,the formed concept can jump in different granular layers and accelerate the rate of the Gaussian cloud transformation effectively.Secondly,the multi-granularity cloud transform algorithm based on the density peaks is used to transform the gray image into cloud model of different granularity.The image is decomposed based on cloud model of different granularity levels,and the foreground and background information are segmented in the coarser granularity layer.Through the decomposition of image space of coarse granularity into several fine ones,more details of the image are segmented on the fine granularity layer.Finally,experimental results show that this algorithm can segment the image accurately,and the segmentation of image details is more prominent than other methods.