DEALER: Distributed Clustering with Local Direction Centrality and Density Measure DOI Creative Commons
Xuze Liu, Ziqi Zhao, Yuhai Zhao

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3988 - 3988

Published: April 4, 2025

Clustering by Measuring Local Direction Centrality (CDC) is a recently proposed innovative clustering method. It identifies clusters assessing the direction centrality of data points, i.e., distribution their k-nearest neighbors. Although CDC has shown promising results, it still faces challenges in terms both effectiveness and efficiency. In this paper, we propose novel algorithm, Distributed with Density Measure (DEALER). DEALER addresses problem weak connectivity using well-designed hybrid metric density. contrast to traditional density-based methods, does not require user-specified neighborhood radius, thus alleviating parameter-setting burden on user. Further, distributed technique empowered z-value filtering, which significantly reduces cost neighbor computations metric, lowering time complexity from O(n2) O(nlogn). Extensive experiments real synthetic datasets validate efficiency our algorithm.

Language: Английский

Design and Implementation of Takeaway Ordering Recommendation System Based on Python and Flask DOI
Jun Cao,

Wendong Yu,

Hongwei Wei

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 277 - 287

Published: Jan. 1, 2025

Language: Английский

Citations

0
DEALER: Distributed Clustering with Local Direction Centrality and Density Measure DOI Creative Commons
Xuze Liu, Ziqi Zhao, Yuhai Zhao

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3988 - 3988

Published: April 4, 2025

Clustering by Measuring Local Direction Centrality (CDC) is a recently proposed innovative clustering method. It identifies clusters assessing the direction centrality of data points, i.e., distribution their k-nearest neighbors. Although CDC has shown promising results, it still faces challenges in terms both effectiveness and efficiency. In this paper, we propose novel algorithm, Distributed with Density Measure (DEALER). DEALER addresses problem weak connectivity using well-designed hybrid metric density. contrast to traditional density-based methods, does not require user-specified neighborhood radius, thus alleviating parameter-setting burden on user. Further, distributed technique empowered z-value filtering, which significantly reduces cost neighbor computations metric, lowering time complexity from O(n2) O(nlogn). Extensive experiments real synthetic datasets validate efficiency our algorithm.

Language: Английский

Citations

0