Name
Abstract | ||
|---|---|---|
Clustering of data with high dimension and variable densities poses a remarkable challenge to the traditional density-based clustering methods. Recently, entropy, a numerical measure of the uncertainty of information, can be used to measure the border degree of samples in data space and also select significant features in feature set. It was used in our new framework based on the sparsity-density ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TKDE.2018.2792021 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Entropy,Clustering algorithms,Clustering methods,Shape,Complexity theory,Data models,Self-organizing feature maps | Data mining,Data modeling,Data set,Data space,Computer science,Feature set,Sampling (statistics),Cluster analysis | Journal |
Volume | Issue | ISSN |
30 | 8 | 1041-4347 |
Citations | PageRank | References |
3 | 0.37 | 0 |
Authors | ||
4 | ||