Refinement After Density-based Clustering on Dirty Data
碩士 === 國立臺灣科技大學 === 資訊工程系 === 106 === Clustering algorithms are efficient for the task of class identification in spatial databases. Noise after clustering sometimes is meaningful due to mistake by inappropriate parameters setting or environmental factor in collecting data, we call them “dirty data”...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2018
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/epsyrq |