Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Abstract

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.

Data stream classification with ant colony optimisation

“…Related works on ACO-based data stream algorithms have mainly focused on clustering, [52][53][54][55] an unsupervised † learning task where the goal is to group data instances based on their features into homogeneous clusters. The only exception, to the best of our knowledge, is the Stream Ant Colony Decision Forrest (strACDF) algorithm proposed in Reference [56] for solving the data stream e-mail foldering problem.…”

IoT based traffic congestion control for environmental applications

“…Clustering Protocol) [19] The authors proposed a clustering protocol for RCSFHs, based on a dual phase CH election scheme. So we have 2 steps to choose the CH of the cluster, the first step is the initial and temporary CH selection based on the primary and residual energy level, and the second step is the process of replacing the temporary CHs with high power CHs to form the final CH of the cluster.…”

“…A connected object has value when it is connected to other objects and software bricks, for example: a connected watch is only of interest within a health/wellness oriented ecosystem, which goes far beyond knowing the time. An CO with three key elements: The data produced or received, stored or transmitted, the algorithms to process this data, the ecosystem in which it will react and integrate.The usage properties of a CO [18], [19]: Ergonomics (usability, handiness).Aesthetics (shapes /colors/ sounds/ sensations). Meta-Morphism(adaptability, personalization modulation).…”