Estimating the number of clusters in a dataset via consensus clustering

Ünlü, Ramazan Erkin; Xanthopoulos, Petros

doi:10.1016/j.eswa.2019.01.074

Cited by 101 publications

(48 citation statements)

References 52 publications

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“…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]. 12,…”

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

View full text Add to dashboard Cite

show abstract

“…Clustering is unsupervised learning for organizing similar data in the same cluster and dissimilar ones in another cluster (Ünlü and Xanthopoulos, 2019). The objective is to group data on the basis of a similar characteristic, in which the members of a single cluster are close to one another with suitable distance between clusters (Kumar and Sahoo, 2014).…”

Section: Related Researchmentioning

confidence: 99%

Modified ACS Centroid Memory for Data Clustering

Jabbar¹,

Ku-Mahamud²,

Sagban³

2019

Journal of Computer Science

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Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm's memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant's movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.

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“…Li et al also proposed a new clustering validity index in 2019, which is based on the deviation ratio of the sum of squares and Euclidean distance [15], and designed a method to dynamically determine the optimal number based on the index. Ramazan and Petros [16] proposed in 2019 to assign weights to multiple clustering algorithms according to data sets and determine the number of clusters by using various indicators. Khan and Luo proposed an algorithm in 2019 to determine the number of clusters in fuzzy clustering by using variable weights [17], which introduced punishment rules and updated variable weights with formulas.…”

Section: Related Workmentioning

confidence: 99%

A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point

Zhang

2019

Information

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Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm.

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Estimating the number of clusters in a dataset via consensus clustering

Cited by 101 publications

References 52 publications

An improved ACS algorithm for data clustering

An improved ACS algorithm for data clustering

Modified ACS Centroid Memory for Data Clustering

A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point

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