| Autor: |
Mbyamm Kiki, Mesmin J, Zhang, Jianbiao, Kouassi, Bonzou Adolphe |
| Předmět: |
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| Zdroj: |
Cluster Computing; Mar2021, Vol. 24 Issue 1, p489-500, 12p |
| Abstrakt: |
Fuzzy C-means clustering integration algorithm is a method to improve clustering quality by using integration ideas, but as the amount of data increases, its time complexity increases. A parallel FCM clustering integration algorithm based on MapReduce is proposed. The algorithm uses a random initial clustering centre to obtain differentiated cluster members. By establishing an overlapping matrix between clusters, the clustering labels are unified to find logical equivalence clusters. The cluster members share the classification information of the data objects by voting to obtain the final clustering result. The experimental results show that the parallel FCM clustering integration algorithm has good performance, and has high speedup and good scalability. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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