Akan veri kümeleme probleminde ağaç veri yapılarının performans karşılaştırması

Şenol, Ali; Kaya, Mahmut; Canbay, Yavuz

doi:10.17341/gazimmfd.1144533

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…S ¸enol and Karacan [14] used the KD-Tree structure to process high-dimensional data. Similarly, S ¸enol et al [34] stated that tree data structures can benefit stream clustering data. However, the major limitations of these methods are their weakness in detecting arbitrary-shaped clusters.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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ANDClust: An Adaptive Neighborhood Distance‐Based Clustering Algorithm to Cluster Varying Density and/or Neck‐Typed Datasets

Şenol

2024

Advcd Theory and Sims

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Although density‐based clustering algorithms can successfully define clusters in arbitrary shapes, they encounter issues if the dataset has varying densities or neck‐typed clusters due to the requirement for precise distance parameters, such as eps parameter of DBSCAN. These approches assume that data density is homogenous, but this is rarely the case in practice. In this study, a new clustering algorithm named ANDClust (Adaptive Neighborhood Distance‐based Clustering Algorithm) is propoesed to handle datasets with varying density and/or neck‐typed clusters. The algorithm consists of three parts. The first part uses Multivariate Kernel Density Estimation (MulKDE) to find the dataset's peak points, which are the start points for the Minimum Spanning Tree (MST) to construct clusters in the second part. Lastly, an Adaptive Neighborhood Distance (AND) ratio is used to weigh the distance between the data pairs. This method enables this approach to support inter‐cluster and intra‐cluster density varieties by acting as if the distance parameter differs for each data of the dataset. ANDClust on synthetic and real datasets are tested to reveal its efficiency. The algorithm shows superior clustering quality in a good run‐time compared to its competitors. Moreover, ANDClust could effectively define clusters of arbitrary shapes and process high‐dimensional, imbalanced datasets may have outliers.

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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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Akan veri kümeleme probleminde ağaç veri yapılarının performans karşılaştırması

Cited by 4 publications

References 41 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

ANDClust: An Adaptive Neighborhood Distance‐Based Clustering Algorithm to Cluster Varying Density and/or Neck‐Typed Datasets

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

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