Multi-source heterogeneous data fusion method and system based on fuzzy C-means clustering algorithm

Abstract

The invention provides a multi-source heterogeneous data fusion method and system based on a fuzzy C-means clustering algorithm. The method comprises the steps that multi-source heterogeneous data and corresponding task information in a specific environment are obtained; converting the obtained multi-source heterogeneous data into descriptive text data, extracting feature segmented words from the descriptive text data, and normalizing the feature segmented words to obtain standard feature information; establishing an event tree according to the obtained multi-source heterogeneous data and the corresponding task information, and calculating the correlation probability of the standard feature information and the event tree based on a multiple correlation coefficient algorithm; and performing feature fusion on the standard feature information by adopting a fuzzy C-means clustering algorithm based on the correlation probability to obtain a fusion result meeting conditions. According to the multi-source heterogeneous data fusion method, the standard feature information is extracted firstly, and then fusion is carried out by adopting the fuzzy C-means clustering algorithm, so that effective fusion of multi-source heterogeneous data under the conditions of complex data types and large data dimensions is realized, and the utilization rate of the multi-source heterogeneous data fusion method in practical application is improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a multi-source heterogeneous data fusion method and system based on a fuzzy C-means clustering algorithm. Background technique [0002] With the popularization of computers and digital electronic products and the rapid development of the Internet, people can come into contact with massive multi-source heterogeneous data every day, integrate multi-source heterogeneous data, and apply based on the fused data, which is conducive to the realization of Scientific decision-making and a wider range of applications. However, due to the different sources, structures, attributes, modalities, and uses of multi-source heterogeneous data, data fusion is more complicated. In order to achieve the goal of valuable applications, there is a need for a method and system that transcends multimodality such as voice, image, video, and impact, and can support multi-source heterogeneous data fu...

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Problems solved by technology

[0004] In view of this, this application proposes a multi-source heterogeneous data fusion method and system based on the fuzzy C-means clustering algorithm, which solves the problems caused by the long time-consuming and high space complexity of the existing multi-source heterogeneous data fusion method. Problems that cannot be widely applied

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