Improvement of K-means Cluster Quality by Post Processing Resulted Clusters.
Ioan-Daniel BorleaRadu-Emil PrecupAlexandra-Bianca BorleaPublished in: ITQM (2021)
Keyphrases
- post processing
- clustering quality
- clustering algorithm
- k means
- hierarchical clustering
- cluster centers
- data clustering
- cluster analysis
- unsupervised clustering
- clustering framework
- clustering approaches
- agglomerative hierarchical clustering
- preprocessing
- constrained clustering
- clustering result
- spectral clustering
- intra cluster
- inter cluster
- document clustering
- clustering method
- davies bouldin
- hierarchical agglomerative clustering
- overlapping clusters
- cluster validity
- fuzzy clustering
- clustering solutions
- self organizing maps
- hierarchical clustering algorithm
- data points
- initial cluster centers
- fuzzy k means
- subspace clustering
- cluster structure
- fuzzy c means
- cluster centroids
- human experts
- fuzzy clustering algorithm
- pattern extraction
- cluster ensemble
- hierarchical clustering algorithms
- projections onto convex sets
- arbitrary shape
- rough k means
- disjoint clusters
- shape error concealment