Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Clustering Algorithm
|
| gptkbp:advantage |
Computationally Expensive
Irreversible Merges or Splits No Need to Specify Number of Clusters Sensitive to Noise |
| gptkbp:application |
gptkb:Image_Segmentation
Social Network Analysis Market Segmentation Document Clustering Gene Expression Analysis |
| gptkbp:canBe |
Deterministic
Non-deterministic Bottom-Up Top-Down Hybrid Approaches Distance Matrix Hierarchical Agglomerative Clustering Hierarchical Divisive Clustering Linkage Criteria Similarity Matrix |
| gptkbp:compatibleWith |
Pre-specified Number of Clusters
|
| gptkbp:complexity |
O(n^3)
|
| gptkbp:doesNotScaleWellWith |
Large Datasets
|
| gptkbp:firstStepAgglomerative |
Each Point is a Cluster
|
| gptkbp:firstStepDivisive |
All Points in One Cluster
|
| gptkbp:hasType |
Agglomerative Clustering
Divisive Clustering |
| gptkbp:heldBy |
gptkb:Greedy_Algorithm
Unsupervised Learning Method |
| https://www.w3.org/2000/01/rdf-schema#label |
Hierarchical Clustering
|
| gptkbp:improves |
Approximate Algorithms
Efficient Data Structures Parallelization |
| gptkbp:linkageCriteria |
Average Linkage
Complete Linkage Single Linkage Ward's Method |
| gptkbp:mergesBasedOn |
Distance or Similarity
|
| gptkbp:output |
Dendrogram
|
| gptkbp:relatedTo |
gptkb:DBSCAN
Spectral Clustering K-means Clustering |
| gptkbp:usedIn |
gptkb:Machine_Learning
gptkb:Bioinformatics Data Mining Image Analysis |
| gptkbp:visualizes |
Dendrogram
|
| gptkbp:bfsParent |
gptkb:Unsupervised_Learning
|
| gptkbp:bfsLayer |
7
|