Abstract
Aiming at the problems of poor security and clustering accuracy in current data clustering algorithms, a controllable clustering algorithm for real-time streaming big data based on multi-source data fusion is proposed. The FIR filter structure model is used to suppress network interference, and ant colony algorithm is used to detect the abnormal data in the big data. By optimizing the iteration, the pheromone concentration is placed in the front position as the abnormal data point, and the filter is introduced. The fusion scope of multi-source data fusion is set. Combined with the data similarity function, the multi-source data fusion concept is used to construct the associated real-time streaming big data fusion device, and the data deduplication results are substituted into the fusion device to obtain the data clustering result. The experiments show that the proposed algorithm has high safety factor, good data clustering accuracy, high clustering efficiency, and low energy consumption.
Highlights
Aiming at the problems existing in the current research results, a controllable clustering algorithm for associated real-time streaming big data based on multi-source data fusion is proposed. e general framework is as follows: (1) e anti-interference filtering of the associated realtime streaming big data is realized by FIR filtering algorithm, and the abnormal data in the filtering result by ant colony algorithm are detected and filtered out to improve the data clustering security in real time
(4) e experiment and discussion method are used to verify the controllable clustering algorithm for associated real-time streaming big data based on multisource data fusion
The performance of relevant research results is to be improved, and a controllable clustering algorithm for associated real-time streaming big data based on multi-source data fusion is proposed
Summary
Aiming at the problems existing in the current research results, a controllable clustering algorithm for associated real-time streaming big data based on multi-source data fusion is proposed. e general framework is as follows:. Aiming at the problems existing in the current research results, a controllable clustering algorithm for associated real-time streaming big data based on multi-source data fusion is proposed. (1) e anti-interference filtering of the associated realtime streaming big data is realized by FIR filtering algorithm, and the abnormal data in the filtering result by ant colony algorithm are detected and filtered out to improve the data clustering security in real time. (2) Redundant data in associated real-time streaming big data are removed to reduce energy consumption of data clustering. (4) e experiment and discussion method are used to verify the controllable clustering algorithm for associated real-time streaming big data based on multisource data fusion
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