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Journals / International Journal of Intelligent Systems and Applications in Engineering / 2017 / Cilt: 5 - Sayı: 4

A New Approach to Determine Eps Parameter of DBSCAN Algorithm

FATMA ÖZGE ÖZKÖK
METE ÇELİK
Pages
247–251
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental evaluations showed that the proposed method outperformed the classicalmethod

📌 3 Key Takeaways

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Objective & Hypothesis:In recent years, data analysis has become important with increasing data volume.
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Key Findings & Data:The values of these parameters significantly affectclustering performanceof the algorithm.
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Conclusion & Impact:The experimental evaluations showed that the proposed method outperformed the classicalmethod
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In recent years, data analysis has become important with increasing data volume. Clustering, which groups objects according to their similarity, has an important role in data analysis. DBSCAN is one of the most effective and popular density-based clustering algorithm and has been successfully implemented in many areas. However, it is a challenging task to determine the input parameter values of DBSCAN algorithmwhich are neighborhood radius Epsand minimum number of points MinPts. The values of these parameters significantly affectclustering performanceof the algorithm. In this study, we proposeAE-DBSCAN algorithm which includes a new method to determine the value of neighborhood radius Epsautomatically. The experimental evaluations showed that the proposed method outperformed the classicalmethod

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Cite This Article

ÖZKÖK, F. Ö., ÇELİK, M. (2017). A New Approach to Determine Eps Parameter of DBSCAN Algorithm. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 5 - Sayı: 4, 247–251. https://ajindex.com/articles/210242

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