A Clustering Approach Using Enhanced K-Means in 5G Networks,ERICDATA高等教育知識庫
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篇名
A Clustering Approach Using Enhanced K-Means in 5G Networks
並列篇名
A Clustering Approach Using Enhanced K-Means in 5G Networks
作者 Min ZhuXin XiaJianming ZhangDengyin Zhang
英文摘要
There have been amount of messages for multiple users that exist in 5G communication networks. In order to provide high-quality services for these users at the same time, the messages for different users need to be grouped by clustering. The clustering approach has been promoting the growth of smart businesses in transmitting applications. For a large-scale data, applying this mining algorithm is severely limited. In this paper, we focused on the K-Means clustering where the Frequent Patterngrowth (FP-growth) algorithm applied to each cluster. The mined frequent items of the short text always have the similar meaning. And the representative candidates based on frequent items can represent the whole cluster of short texts that they belong to. This method of an enhanced K-Means based on FP-Growth breakthrough the limit of assuming the number of clusters, k, known in advance. It can automatically find as many candidates of short sentence as possible, and runs much faster and consumes much less main memory than the general algorithm by the computing distances between text documents like Earth Mover’s Distance based on Word2Vector.
起訖頁 1885-1892
關鍵詞 ClusteringShort text clusteringK-MeansFP-Growth
刊名 網際網路技術學刊  
期數 202012 (21:7期)
出版單位 台灣學術網路管理委員會
DOI 10.3966/160792642020122107003   複製DOI
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