دانلود رایگان مقاله انگلیسی یک الگوریتم خوشه بندی همگانی مبتنی بر فاصله مینکوفسکی جدید - اشپرینگر 2017

Abstract:

Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number is still an open problem. In this paper, we propose a novel consensus clustering algorithm that is based on the Minkowski distance. Fusing with the Newman greedy algorithm in complex networks, the proposed clustering algorithm can automatically set the number of clusters. It is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. A numerical simulation is also given to demonstrate the effectiveness of the proposed algorithm. Finally, this consensus clustering algorithm is applied to a froth flotation process.

6 Conclusions

Consensus clustering can solve the problem of reconciling clustering information about the same data set that arises from different runs of the same algorithm. Then, it can find a single consensus clustering that is better than the existing clusters. In this paper, we propose a novel consensus clustering algorithm that considers the consensus clustering partition distance and similarity matrix. Based on the Minkowski distance, the proposed clustering algorithm can automatically set the number of clusters and obtain better clustering results, which can find a compromise in the different clustering information about the same data set. Numerical simulation results are provided to demonstrate the effectiveness of the presented algorithm. This real application also verifies the effectiveness of the proposed consensus clustering algorithm.