A New Incremental Semi-Supervised Graph Based Clustering
| Autor: | Fedor Pashchenko, Thang Vu-Viet, Vu Viet Thang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
DBSCAN
Data stream Graph based clustering Computer science Dynamic data 02 engineering and technology computer.software_genre Fuzzy logic ComputingMethodologies_PATTERNRECOGNITION Data point 020204 information systems 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Data mining Cluster analysis computer |
| Zdroj: | 2018 Engineering and Telecommunication (EnT-MIPT). |
| DOI: | 10.1109/ent-mipt.2018.00054 |
| Popis: | Incremental clustering or one-pass clustering is very useful when we work with data stream or dynamic data. In each incremental clustering algorithm, two process including insertion and deletion for new data points are used for updating the current clusters. In fact, for traditional clustering such as K-Means, Fuzzy C-Means, DBSCAN, etc., many versions of incremental clustering have been developed. However, to the best of our knowledge, there are no incremental semi-supervised clustering in literature. This paper introduces a new incremental semi-supervised clustering which was based on a graph of k-nearest neighbor using seeds, namely IncrementalSSGC. Experiments conducted on some data sets from UCI and the 802.11 network data set (AWID) show the effectiveness of our new IncrementalSSGC. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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