Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Şenol, Ali; Karacan, Hacer

doi:10.31590/ejosat.446019

Cited by 6 publications

(1 citation statement)

References 72 publications

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“…Stream clustering is an efficient clustering approach that can cluster streaming data fast according to similarity criteria and update the clusters based on the characteristics of the data. Applications of these approaches include clickstream analysis, intrusion detection systems, social media, financial applications, scientific research, health research, mobile applications, the Internet of Things (IoT), and sensor networks [6].…”

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

Şenol

2023

Neural Comput & Applic

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Detection of Turkish Fake News in Twitter with Machine Learning Algorithms

2021

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Social media has affected people's information sources. Since most of the news on social media is not verified by a central authority, it may contain fake news for various reasons such as advertising and propaganda. Considering an average of 500 million tweets were posted daily on Twitter alone in the year of 2020, it is possible to control each share only with smart systems. In this study, we use Natural Language Processing methods to detect fake news for Turkish-language posts on certain topics on Twitter. Furthermore, we examine the follow/follower relations of the users who shared fake-real news on the same subjects through social network analysis methods and visualization tools. Various supervised and unsupervised learning algorithms have been tested with different parameters. The most successful F1 score of fake news detection was obtained with the support vector machines algorithm with 0.9. People who share fake/true news can help in the separation of subgroups in the social network created by people and their followers. The results show that fake news propagation networks may show different characteristics in their own subject based on the follow/follower network.

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Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Cited by 6 publications

References 72 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

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

Detection of Turkish Fake News in Twitter with Machine Learning Algorithms

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