Neural network learning improvement using K-means clustering algorithm to improve the performance of web traffic mining

Web has become today not only an accessible and searchable information source but also one of the most important communication channels, almost a virtual society. Web mining is a challenging activity that aims to discover new, relevant and reliable information and knowledge by investigating the web structure, its content and its usage. Though the web mining process is similar to data mining, the techniques, algorithms, and methodologies used to mine the web encompass those specific to data mining, mainly because the web has a great amount of unstructured data and the changes are frequent and rapid. In the present work, we propose a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism to discover and analyze useful knowledge from the available Web log data.