Name
Abstract | ||
|---|---|---|
Because of today's explosive information from Internet, people will contact much new information at any moment. So how to analyze this non-stationary information becomes more and more important. Clustering analysis is a good information analysis method, but many clustering algorithms only fit to stationary situation. Then in this paper, a novel incremental clustering algorithm based on self-organizing-mapping-IGSOM is provided to dispose this non-stationary information. This algorithm first uses self-organizing-mapping algorithm to construct a neuron model from original data. Then it selects some sample data from this neuron model, and combines the samples with new coming data together to train a new neuron model. To solve unbalance between sample data and new coming data, it alters sample data's weights. The experiments demonstrate that this incremental clustering method can dispose non-stationary data well, and has relatively high precision. Because only small samples are selected to replace large-scale original data, clustering time is also short. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IIH-MSP.2008.101 | IIH-MSP |
Keywords | Field | DocType |
incremental clustering,incremental clustering based on self-organizing-mapping,pattern clustering,neuron model,stationary data,large-scale original data,new information,clustering analysis,good information analysis method,new coming data,igsom,sample data,explosive information,internet,non-stationary information,nonstationary information,self-organising feature maps,sample data selection,information analysis method,self organizing mapping,original data,cluster sampling,data models,information analysis,cluster analysis,data clustering,algorithm design and analysis,testing,clustering algorithms | Data mining,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Computer science,Determining the number of clusters in a data set,Constrained clustering,Artificial intelligence,Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-0-7695-3278-3 | 1 | 0.36 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Liu | 1 | 963 | 40.79 |
| Yuanchao Liu | 2 | 60 | 6.07 |
| Xiaolong Wang | 3 | 1208 | 115.39 |