MACOC: A Medoid-Based ACO Clustering Algorithm

Menéndez, Héctor D.; Otero, Fernando E. B.; Camacho, David

doi:10.1007/978-3-319-09952-1_11

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…Yang and Kamel (2006) illustrated a multi-ant colonies procedure for clustering based on parallel and independent ant colonies. Menéndez et al (2014) discussed the evolutionary algorithmic approach used by various famous bio-inspired methods to deal with clustering predicament. ATTA an adaptive clustering algorithm to cluster data on the basis of corpse's behavior of ants was discussed (Handl et al, 2003).…”

Section: Ant Colony Optimization In Clusteringmentioning

confidence: 99%

ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

Singh¹,

Singh²,

Pant³

2019

Int J Math, Eng, Manag Sci

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Big Data is rapidly gaining impetus and is attracting a community of researchers and organization from varying sectors due to its tremendous potential. Big Data is considered as a prospective raw material to acquire domain specific knowledge to gain insights related to management, planning, forecasting and security etc. Due to its inherent characteristics like capacity, swiftness, genuineness and diversity Big Data hampers the efficiency and effectiveness of search and leads to optimization problems. In this paper we explore the complexity imposed by big search spaces leading to optimization issues. In order to overcome the above mentioned issues we propose a hybrid algorithm for Big Data preprocessing ACO-clustering algorithm approach. The proposed algorithm can help to increase search speed by optimizing the process. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency.

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Section: Ant Colony Optimization In Clusteringmentioning

confidence: 99%

ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

Singh¹,

Singh²,

Pant³

2019

Int J Math, Eng, Manag Sci

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“…The Medoid-based ACO Clustering Algorithm (MACOC) which is an extension of the ACOC algorithm is proposed. MACOC is medoid-based instead of centroid-based which improves the algorithm to be more robust in the presence of noise [62]. The algorithm exceeds ACOC algorithm performance but suffers from sensitivity for a predefined number of clusters.…”

Section: Ant-based Sorting Versus Aco-based Clustering Approachesmentioning

confidence: 99%

“…Comparison that has been performed and reported is based on individual work with one or two clustering approaches [58]. Several works consider the Shelokar dataset as a benchmark dataset in conducting their studies on ACO-based clustering [59,[60][61][62].…”

Section: Ant-based Sorting Versus Aco-based Clustering Approachesmentioning

confidence: 99%

Ant-based sorting and ACO-based clustering approaches: A review

Jabbar

Ku-Mahamud

Sagban

2018

2018 IEEE Symposium on Computer Applications &Amp; Industrial Electronics (ISCAIE)

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Data clustering is used in a number of fields including statistics, bioinformatics, machine learning exploratory data analysis, image segmentation, security, medical image analysis, web handling and mathematical programming. Its role is to group data into clusters with high similarity within clusters and with high dissimilarity between clusters. This paper reviews the problems that affect clustering performance for deterministic clustering and stochastic clustering approaches. In deterministic clustering, the problems are caused by sensitivity to the number of provided clusters. In stochastic clustering, problems are caused either by the absence of an optimal number of clusters or by the projection of data. The review is focused on ant-based sorting and ACO-based clustering which have problems of slow convergence, un-robust results and local optima solution. The results from this review can be used as a guide for researchers working in the area of data clustering as it shows the strengths and weaknesses of using both clustering approaches.

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“…• Finally, a generation of Ant Colony Optimization algorithms that uses our own model to reduce the complexity problem in Constraint Satisfaction Problems [56], [57], and the application of this ACO approaches to meodoid-based clustering methods [58].…”

Section: Current Trends and New Bio-inspired Approachesmentioning

confidence: 99%

Bio-inspired clustering: Basic features and future trends in the era of Big Data

Camacho

2015

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)

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Clustering is perhaps one of the most popular approaches used in unsupervised machine learning. There's a huge number of different methods and algorithms that have been designed in the last decades related to this "blind pattern search", some of these approaches are based on bio-inspired methods such as Evolutionary Computation, Swarm Intelligence or Neural Networks among others. In the last years, and due to the fast growing of Big Data problems, some interesting advances and new approaches are currently being developed in this area, new algorithms like online clustering and streaming clustering are appearing. These new algorithms try to solve classical problems in Clustering and deal with the new features of these new kind of problems. This keynote lecture will provide some basics on both, Clustering methods and bio-inspired computation, and how they have been combined to improve the quality of these algorithms, to later show the main features that Big Data needs to obtain reliable clustering approaches. Finally, some practical examples and applications will be described to show how these new algorithms are evolving to be used in the near future in complex and dynamic environments.

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MACOC: A Medoid-Based ACO Clustering Algorithm

Cited by 18 publications

References 20 publications

ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

Ant-based sorting and ACO-based clustering approaches: A review

Bio-inspired clustering: Basic features and future trends in the era of Big Data

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