Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The interest of b users can be revealed by the visited b pages and time duration on these b pages during their surfing. , each b access pattern from b logs is transformed into a fuzzy vector with predetermined dimension, each component being a fuzzy linguistic variable or 0 representing the visited b page and the time duration on this b page. Fuzzy simulation is used to compute the distance beten any two fuzzy vectors. Considering the clustering time and efficiency,  propose an evolutionary two-layer clustering algorithm. At the first layer, the learning vector quantization (LVQ) approach is exploited to group the patterns from b logs into a number of clusters. At the second layer, the ighted fuzzy c-means approach is developed to deal with the results of the first layer. In addition, PSO algorithm is adopted to optimize the clustering results. The effectiveness and feasibility of the approach are demonstrated by the algorithm analysis and our experimental results.
['Rui Wu']
I am writing a research paper on a idea Clustering Web Access Patterns Based on Hybrid Approach
[{"from": "human", "value": "I am writing a research paper on a idea Clustering Web Access Patterns Based on Hybrid Approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The interest of b users can be revealed by the visited b pages and time duration on these b pages during their surfing. , each b access pattern from b logs is transformed into a fuzzy vector with predetermined dimension, each component being a fuzzy linguistic variable or 0 representing the visited b page and the time duration on this b page. Fuzzy simulation is used to compute the distance beten any two fuzzy vectors. Considering the clustering time and efficiency,  propose an evolutionary two-layer clustering algorithm. At the first layer, the learning vector quantization (LVQ) approach is exploited to group the patterns from b logs into a number of clusters. At the second layer, the ighted fuzzy c-means approach is developed to deal with the results of the first layer. In addition, PSO algorithm is adopted to optimize the clustering results. The effectiveness and feasibility of the approach are demonstrated by the algorithm analysis and our experimental results."}]