Continuous greedy randomized adaptive search procedure for data clustering

Queiroga, Eduardo; Subramanian, Anand; Cabral, Lucídio dos Anjos Formiga

doi:10.1016/j.asoc.2018.07.031

Cited by 11 publications

(1 citation statement)

References 40 publications

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“…The idea that the nearest neighbors can be used for retaining the clustering quality is based on studies which indicate that the good clusters should have the most minimal sum of the euclidean distance between the objects and their cluster's centroid [11]- [13], [21], [27]. Based on that, the objects within the same cluster must be neighboring and near to each other.…”

Section: The Objective and Contributionmentioning

confidence: 99%

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Lucky

Girsang

2020

IJCAI

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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. Povzetek: Predstavljen je hibridni algoritem najbližjih sosedov z optimizacijo mravljinčnih kolonij za grupiranje komentarjev socialnih medijev.

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