In order to save time in extracting specific information from high volume of data in web documents, this paper proposes an architectural model of generic web document classification system using design patterns for classifying web documents. This work implements two classification techniques for classifying Thai web documents, namely centroid classification and neural network classification, based on the proposed model and compares their classification effectiveness empirically. The training data sets in this experiment consist of 500 web documents of the following five categories (100 documents for each category): mobile phone sales, book sales, travel sales, education information and company profile. Another two hundred and fifty web documents were then used to test the two classifiers. The experiment results showed that the centroid classifier outperforms the neural network classifier both in term of efficiency and effectiveness

In order to reduce time to find specific information from high volume of information on the Web, this paper proposes the implementation of an automatic identification of specific Web documents by using centroid technique. The Initial training sets in this experiment are 4113 Thai e-Commerce Web documents. After training process, the system gets a Centroid e-Commerce vector. In order to evaluate the system, six test sets were taken under consideration. In each test set has 100 Web pages both known e-Commerce and non e-Commerce Web pages. The average system performance is about 90 %.