Research on Big Data Text Clustering Algorithm Based on Swarm Intelligence

In order to break through the limitations of current clustering algorithms and avoid the direct impact of disturbance on the clustering effect of abnormal big data texts, a big data text clustering algorithm based on swarm intelligence is proposed. According to the characteristics of swarm intelligence, a differential privacy protection model is constructed; At the same time, the data location information operation is carried out to complete the location information preprocessing. Based on KD tree division, the allocation of the differential privacy budget is completed, and the location information is clustered by dimension reduction. Based on the result of the location information dimension reduction clustering, a differential privacy clustering algorithm is proposed to maximize the differentiation of the clustering effect. Select the fuzzy confidence and support thresholds to extract the associated features, use the features to determine the big data text targets to be clustered, establish a target decision matrix, determine the clustering targets, and obtain the target weights, the obtained membership, and the decision results. Realize the effective clustering of big data text based on swarm intelligence. The experimental results show that the algorithm can effectively realize the high-efficiency clustering of big data texts, and the clustering time is short, and it has a good clustering effect.