Research on Data Stream Clustering Algorithm Based on Interactive Basis Function
Clustering is an effective tool for data mining,and data stream clustering has become a hot topic in current research.Currently,many data stream clustering algorithms have been proposed,but most of them use distance as a similarity metric,which is sensitive to noise,and not ideal in clustering effect.In order to enhance the flexibility and improve the clustering quality of data flow clustering algo-rithms,we introduce fractional order interactive basis functions(IBFs)into data flow clustering,and combine them with fuzzy ART algorithm for expansion to generate flexible decision edge strategies.A novel data flow clustering algorithm,IBFs_ART,is proposed.The algorithm first expands the arrived data points through a pre calculated function based on the correlation between features,and performs fractional transformation on the original features.Then,it clusters the data streams based on interactive basis functions.Interactive basis functions can generate flexible decision boundaries without specifying software.Precomputing functions can be implemented in any algorithm,and can be used for any extension of data stream clustering algorithms.Experiments have shown that using IBFs can achieve lower computational costs and generate flexible decision boundaries to find the optimal clustering,achieve higher clustering quality and purity under the same alert parameters,and have higher clustering accuracy,symmetry metrics,and smaller error rates compared with traditional clustering algorithms.
clusterdata streamdata stream clusteringinteractive basis functionfuzzy adaptive resonance theory
万方数据
维普
