Fuzzy hierarchical clustering algorithm facing large databases

Applying fuzzy theory into hierarchical clustering method, we presented a fuzzy hierarchical clustering algorithm. After datasets were divided into several sub-clusters using partitioning method, a fuzzy graph of sub-clusters was constructed by analyzing the linked fuzzy degree among the sub-clusters. By making λ cut graph for the fuzzy graph, we got the connected components of the fuzzy graph, which was the result of clustering we wanted to get. The algorithm could be performed in high-dimensional data set to cluster the arbitrary shape of clusters. Furthermore, not only could this algorithm dispose the data with numeric attributes, but with categorical attributes also. The results of our experimental study in data sets with arbitrary shape and size are very encouraging. We have also conducted an experimental study with Web log files that could help us to discover the user access patterns effectively. Our study shows that this algorithm generates better quality clusters than traditional algorithms, and scales well for large databases.