Self-Adaptive K-Means Based on a Covering Algorithm
The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial c...
Saved in:
Main Authors: | Yiwen Zhang, Yuanyuan Zhou, Xing Guo, Jintao Wu, Qiang He, Xiao Liu, Yun Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2018-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2018/7698274 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An improved K‐means algorithm for big data
by: Fatemeh Moodi, et al.
Published: (2022-02-01) -
Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms
by: Nurfidah Dwitiyanti, et al.
Published: (2024-12-01) -
Bearing Fault Diagnosis Method based on Self-adaptive Stochastic Resonance of Genetic Algorithm and VMD
by: Zhang Chao, et al.
Published: (2018-01-01) -
Improved K-Means Algorithm for Nearby Target Localization
by: Zongwen Yuan, et al.
Published: (2025-01-01) -
Application of K-means algorithm based on A-D model in calling abnormal customer mining
by: Jian ZHOU, et al.
Published: (2018-04-01)