A weighted k-mean clustering algorithm based on singular values with offset clustering centers DOI Creative Commons
Shaobo Deng,

Xing Lin,

Wei-Li Yuan

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Abstract The K-means algorithm is widely used for dataset clustering, but it does not consider the importance of each attribute dimension when dealing with feature attributes and clustering center selection, rather treats all as having equal importance. In order to solve this problem, paper proposes a weighted k-mean (SVW-KMeans) based on singular values offset centers. calculates weight information data points through value decomposition focus most significant different features, joining calculation optimize objective function, at same time, arithmetic mean individuals center, shifted towards high so take into full consideration features in process. experimental results show that SVW-KMeans outperforms other algorithms synthetic real datasets, which verifies mainstream terms quality stability.

Language: Английский

Brahmaputra River (Pandu Station) Flow Prediction Using MLR, ANN, and RF Models Combined with Wavelet Transform DOI Creative Commons
Sachin Dadu Khandekar, Dinesh Shrikrishna Aswar, Varsha Sachin Khandekar

et al.

KSCE Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: 28(11), P. 5396 - 5408

Published: Aug. 6, 2024

Language: Английский

Citations

0
A weighted k-mean clustering algorithm based on singular values with offset clustering centers DOI Creative Commons
Shaobo Deng,

Xing Lin,

Wei-Li Yuan

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Abstract The K-means algorithm is widely used for dataset clustering, but it does not consider the importance of each attribute dimension when dealing with feature attributes and clustering center selection, rather treats all as having equal importance. In order to solve this problem, paper proposes a weighted k-mean (SVW-KMeans) based on singular values offset centers. calculates weight information data points through value decomposition focus most significant different features, joining calculation optimize objective function, at same time, arithmetic mean individuals center, shifted towards high so take into full consideration features in process. experimental results show that SVW-KMeans outperforms other algorithms synthetic real datasets, which verifies mainstream terms quality stability.

Language: Английский

Citations

0