A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm.
Congming ShiBingtao WeiShoulin WeiWen WangHai LiuJialei LiuPublished in: EURASIP J. Wirel. Commun. Netw. (2021)
Keyphrases
- clustering algorithm
- dynamic programming
- clustering method
- computational complexity
- high accuracy
- hierarchical clustering
- preprocessing
- detection method
- closed form
- discriminant features
- unsupervised clustering
- data clustering
- probabilistic model
- cost function
- k means
- pairwise
- similarity measure
- feature extraction
- knn
- dimensionality reduction
- unsupervised learning
- significant improvement
- fuzzy c means
- cluster centers
- neural network