Combining Web Data Extraction and Data Mining Techniques to Discover Knowledge

The design and implementation of a support system for Knowledge Discovery is the challenge of many researchers. As Data Mining is the main key step in Knowledge Discovery process in Databases (KDD), it is necessary to find a new methodology that combines web data extraction playing the role of data collection from the web and data mining techniques on the extracted categorical data in order to discover knowledge. The main contribution of this research is proposing a methodology to apply the clustering notion on categorical web data and to use the clustering results as part of the input for the classification conducted on another set of data. Data mining and relative data processing are conducted by developing intelligent tools. The performance of the algorithms used in our methodology is demonstrated with the clustered job postings dataset and classified job searchers dataset by using the three measures accuracy, recall and precision for the clustering algorithm and the error of classification for the classification technique. The results show that our proposed approach of combination ends up with good results in Knowledge Discovery from the web.