MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters

Şenol, Ali

doi:10.1007/s00521-023-08386-3

Neural Comput & Applic

2023

DOI: 10.1007/s00521-023-08386-3

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MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters

Ali Şenol

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2024

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Cited by 6 publications

References 37 publications

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MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

Erdinç,

Kaya,

Şenol

2024

Neural Comput & Applic

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Stream clustering has emerged as a vital area for processing streaming data in real-time, facilitating the extraction of meaningful information. While efficient approaches for defining and updating clusters based on similarity criteria have been proposed, outliers and noisy data within stream clustering areas pose a significant threat to the overall performance of clustering algorithms. Moreover, the limitation of existing methods in generating non-spherical clusters underscores the need for improved clustering quality. As a new methodology, we propose a new stream clustering approach, MCMSTStream, to overcome the abovementioned challenges. The algorithm applies MST to micro-clusters defined by using the KD-Tree data structure to define macro-clusters. MCMSTStream is robust against outliers and noisy data and has the ability to define clusters with arbitrary shapes. Furthermore, the proposed algorithm exhibits notable speed and can handling high-dimensional data. ARI and Purity indices are used to prove the clustering success of the MCMSTStream. The evaluation results reveal the superior performance of MCMSTStream compared to state-of-the-art stream clustering algorithms such as DenStream, DBSTREAM, and KD-AR Stream. The proposed method obtained a Purity value of 0.9780 and an ARI value of 0.7509, the highest scores for the KDD dataset. In the other 11 datasets, it obtained much higher results than its competitors. As a result, the proposed method is an effective stream clustering algorithm on datasets with outliers, high-dimensional, and arbitrary-shaped clusters. In addition, its runtime performance is also quite reasonable.

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MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

Erdinç,

Kaya,

Şenol

2024

Neural Comput & Applic

Self Cite

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A new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis

Şenol,

Talan,

Aktürk

2024

SIViP

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Developing a secure image encryption technique using a novel S-box constructed through real-coded genetic algorithm’s crossover and mutation operators

Ustun,

Sahinkaya,

Atli

2024

Expert Systems with Applications

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MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters

Cited by 6 publications

References 37 publications

MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

A new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis

Developing a secure image encryption technique using a novel S-box constructed through real-coded genetic algorithm’s crossover and mutation operators

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