Retraction Note: Research on semi supervised K-means clustering algorithm in data mining

Mai, Xiaodong; Cheng, Jimin; Wang, Shengnan

doi:10.1007/s10586-022-03923-6

Cited by 2 publications

(2 citation statements)

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“…Clustering aims to maximize data similarity within clusters while minimizing similarity between different clusters [4,5]. Until now, previous researchers have proposed lots of classic clustering algorithms [6][7][8][9][10][11][12][13]. For instance, clustering algorithms like Kmeans [14], DBSCAN [15], and FCM [16] are widely recognized.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical clustering algorithm based on natural local density peaks

Cai,

Feng,

Yang

et al. 2024

Preprint

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Natural neighbor is known for its ability to adaptively identify the nearest neighbors in a dataset, but it sometimes can not accurately cluster in datasets with density variations. To overcome this drawback, this paper proposes a hierarchical clustering algorithm based on natural local density peaks (HC-NLDP). HC-NLDP mainly consists of partition and merge. Firstly, HC-NLDP searches natural local density peaks to form sub-clusters. This effectively solves the problem that points in low-density regions are difficult to cluster accurately. Secondly, HC-NLDP computes the shared similarity between every pair of sub-clusters. The shared similarity describes the relevance between sub-clusters. When confronted with datasets exhibiting density variations, the shared similarity can ensure that HC-NLDP achieves superior clustering performance. Numerical experiments based on synthetic and real datasets demonstrate the effectiveness and superiority of HC-NLDP.

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Section: Introductionmentioning

confidence: 99%

Hierarchical clustering algorithm based on natural local density peaks

Cai,

Feng,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It is also associated with the probability-based selection (Pbselection) approach that allocates unassigned data points in every iteration. The paper by [ 83 ] has revealed a semi-supervised K clustering framework, whereby a K-means clustering framework is initially used for the gene data. Following this, an enhanced semi-supervised K means clustering is implemented for greedy iteration to identify the K mean clustering and obtain improved outcomes.…”

Section: Introductionmentioning

confidence: 99%

Multi-population Black Hole Algorithm for the problem of data clustering

Salih¹,

Alsewari

Wahab³

et al. 2023

PLoS ONE

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The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.

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Retraction Note: Research on semi supervised K-means clustering algorithm in data mining

Cited by 2 publications

References 0 publications

Hierarchical clustering algorithm based on natural local density peaks

Hierarchical clustering algorithm based on natural local density peaks

Multi-population Black Hole Algorithm for the problem of data clustering

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