Abstract
Aiming at the low efficiency, poor performance and weak stability of traditional clustering algorithms and the poor response to the processing of massive data in real time, a real-time streaming controllable clustering edge computing algorithm (SCCEC) is proposed. First, the data tuples that arrive in real time are pre-processed by coarse clustering, the number of clusters, and the position of the center point are determined, and a set formed by macro clusters having differences is formed. Secondly, the macro cluster set obtained by the coarse clustering is sampled, and then K-means parallel clustering is performed with the largest and smallest distances, thereby realizing fine clustering of data. Finally, the completely clustering algorithm and the edge-computing algorithm are combined to realize the clustering analysis under the edge-computing framework. The experimental results show that the proposed algorithm has the advantages of high efficiency, good quality, and strong stability. It can quickly obtain the global optimal solution, and deal with massive data with high real-time performance. It can be used for real-time streaming data aggregation under big data background.
Highlights
The storage mechanism mainly includes some tapes, optical discs, and the like
The data tuples that arrive in real time are pre-processed by coarse clustering, and the number of clusters and the position of the center point are determined, and a set formed by macro clusters having differences is formed
The macro cluster set obtained by the coarse clustering is sampled, and K-means parallel clustering is performed with the largest and smallest distances, thereby realizing fine clustering of data
Summary
The storage mechanism mainly includes some tapes, optical discs, and the like. people used this approach as big data at the time, from the perspective of today’s data traffic, the storage of these data will undoubtedly be very limited and very limited in terms of operation [1], [2]. High-quality, efficient clustering of these real-time streaming big data is a major task. It is of great significance to study a real-time streaming steerable clustering algorithm for big data [3], [8]. Aiming at the disadvantages of low efficiency, poor performance, and weak stability of traditional clustering algorithms, a real-time flow controllable clustering edge computing algorithm (SCCEC) for big data is proposed. The algorithm realizes cluster analysis of real-time streaming data through two clusters. The technical contributions of our paper can be concluded as follows: This paper proposes a real-time streaming steerable clustering algorithm for big data combined with edge computing.
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