基于ACO-FFDP的大数据增量聚类分析

Fadwa Bouhafer, M. Heyouni, Anass El Haddadi, Zakaria Boulouard
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引用次数: 0

摘要

动态信息分析的发展,如增量聚类,正在成为大数据中一个非常重要的关注点。在本文中,我们将提出一种新的增量聚类算法,称为“ACO-FFDP-Incremental-Cluster”。该算法是我们团队开发的大型图形可视化算法“FFDP”与“蚁群算法”的结合。FFDP会设置一个大图形的均衡定位;然后它将以坐标向量的形式提供节点的最终位置。蚁群算法将考虑这个向量,并尝试为新数据找到可能的最佳聚类配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ACO-FFDP in incremental clustering for big data analysis
The development of dyamic information analysis, like incremental clustering, is becoming a very important concern in big data. In this paper, we will propose a new incremental clustering algorithm, called "ACO-FFDP-Incremental-Cluster". This algorithm is a combination between "FFDP" a large graph visualization algorithm developed by our team, and "ACO Algorithm". FFDP will set an equilibrium positioning of the large graph; then it will provide the nodes final positions as a vector of coordinates. ACO algorithm will take this vector into consideration and try to find the best clustering configuration possible for new data.
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