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Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments
Informatica ( IF 2.9 ) Pub Date : 2020-03-15 , DOI: 10.31449/inf.v44i1.2672
Lucky Lucky , Abba Suganda Girsang

Ant colony optimization (ACO) is one of robust algorithms for solving optimization problems, including clustering. However, high and redundant computation is needed to select the proper cluster for each object, especially when the data dimensionality is high, such as social media comments. Reducing the redundant computation may cut the execution time, but it can potentially decrease the quality of clustering. With the basic idea that nearby objects tend to be in the same cluster, the nearest neighbors method can be used to choose the appropriate cluster for some objects efficiently by considering their neighbor’s cluster. Therefore, this paper proposes the combination of nearest neighbors and ant colony optimization for clustering (NNACOC) which can reduce the computation time but is still able to retain the quality of clustering. To evaluate its performance, NNACOC was tested using some benchmark datasets and twitter comments. Most of the experiments show that NNACOC outperformed the original ant colony optimization for clustering (ACOC) in quality and execution time. NNACOC also yielded a better result than k-means when clustering the twitter comments.

中文翻译:

用于聚类社交媒体评论的混合最近邻蚁群优化

蚁群优化 (ACO) 是一种用于解决优化问题(包括聚类)的稳健算法。然而,需要大量的冗余计算来为每个对象选择合适的集群,尤其是在数据维数较高的情况下,例如社交媒体评论。减少冗余计算可能会减少执行时间,但它可能会降低聚类的质量。基于附近对象往往在同一个簇中的基本思想,最近邻方法可以通过考虑其邻居的簇来有效地为某些对象选择合适的簇。因此,本文提出了最近邻与蚁群优化相结合的聚类算法(NNACOC),在减少计算时间的同时,仍能保持聚类的质量。为了评估其性能,NNACOC 使用一些基准数据集和 Twitter 评论进行了测试。大多数实验表明,NNACOC 在质量和执行时间上优于原始蚁群优化聚类(ACOC)。在对推特评论进行聚类时,NNACOC 也产生了比 k-means 更好的结果。
更新日期:2020-03-15
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