Many-objective fuzzy centroids clustering algorithm for categorical data

Zhu, Shuwei; Xu, Lihong

doi:10.1016/j.eswa.2017.12.013

Cited by 48 publications

(30 citation statements)

References 53 publications

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“…The F-measure is applied to identify the quality of clustering results and the best value if maximised. It requires two additional measure criteria to calculate its results as shown in (12). These measures are called precision and recall, which can be calculated as shown in (13) and (14), respectively [31].…”

Section: Resultsmentioning

confidence: 99%

“…Given the shortcoming of this approach, researchers have focused on using the metaheuristic approach, which is inspired by insects and their natural behaviour. The metaheuristic approach uses a completely different clustering method wherein the clustering problem is formulated as an optimisation problem [12][13][14]. This approach minimises or maximises an objective function to find the maximum similarity amongst data [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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An improved ACS algorithm for data clustering

Jabbar

Ku-Mahamud

Sagban

2020

IJEECS

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<span lang="EN-GB">Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms. </span>

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved ACS algorithm for data clustering

Jabbar

Ku-Mahamud

Sagban

2020

IJEECS

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“…, … ], where stands for the number of attributes. For categorical data, the object at attribute , ∈ , takes one value from the unordered, discrete set = { : ∈ [1, ]} of possible values [21]. In order to establish a similarity measure between categorical objects, the simple matching function aggregates the number matching values [3]:…”

Section: Weighted Pairing Distance (W-p)mentioning

confidence: 99%

A space-structure based dissimilarity measure for categorical data

Hernández

Cárdenas-Peña

Orozco

2021

IJECE

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The development of analysis methods for categorical data begun in 90's decade, and it has been booming in the last years. On the other hand, the performance of many of these methods depends on the used metric. Therefore, determining a dissimilarity measure for categorical data is one of the most attractive and recent challenges in data mining problems. However, several similarity/dissimilarity measures proposed in the literature have drawbacks due to high computational cost, or poor performance. For this reason, we propose a new distance metric for categorical data. We call it: Weighted pairing (W-P) based on feature space-structure, where the weights are understood like a degree of contribution of an attribute to the compact cluster structure. The performance of W-P metric was evaluated in the unsupervised learning framework in terms of cluster quality index. We test the W-P in six real categorical datasets downloaded from the public UCI repository, and we make a comparison with the distance metric (DM3) method and hamming metric (H-SBI). Results show that our proposal outperforms DM3 and H-SBI in different experimental configurations. Also, the W-P achieves highest rand index values and a better clustering discriminant than the other methods.

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“…where and are the coefficient vectors which calculated by (8) and (9) respectively, is the position of the victim, and is the current position of the gray wolf.…”

Section: Grey Wolf Optimizer Algorithmmentioning

confidence: 99%

“…Yang and Jiang improved a new approach to solve the initialization and automated model selection problems which are encountered by the Hidden markov model (HMM) based clustering [7]. Zhu and Xu proposed a new method called many objective fuzzy centroids clustering algorithm for categorical data by using reference point based genetic algorithm [8]. Karami and Zapata used k-means and particle swarm optimization (PSO) algorithms to improve a hybrid clustering algorithm to get the optimum number of the clusters and achieve good clustering results on this [9].…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

Karakoyun

Inan²,

Akto

2019

ijisae

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The clustering which is an unsupervised classification method is very important for data processing applications. The main purpose of the clustering is to separate the data samples into different groups by using the similarity (or dissimilarity) between data samples. There are many conventional and heuristic algorithms which are used for the clustering problem. Nevertheless, in last years, it is seen that many new techniques are proposed and improved to solve the clustering problem. In this paper, grey wolf optimization (GWO) algorithm which is modelled according to the social behavior of grey wolves is applied to partition the data samples by searching the optimal center of the clusters. The clustering performance of the GWO is compared with the performances of the three clustering algorithms: k-means, kmedoids and fuzzy c-means algorithms. The experiments show that the GWO algorithm has generally better results than the other clustering algorithms and can be alternatively applied on the clustering problem.

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Many-objective fuzzy centroids clustering algorithm for categorical data

Cited by 48 publications

References 53 publications

An improved ACS algorithm for data clustering

An improved ACS algorithm for data clustering

A space-structure based dissimilarity measure for categorical data

Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

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