An Association Rules Mining Algorithm on Context-Factors and Users' Preference

Pervasive computing is a computing method which emphasizes people oriented. This method advocates the idea that computing has to meet humans' habits. Context aware is one of core technology of pervasive computing. Association rules mining is a method that mine and detect the relation between event with another event or item from mass datasets. However, traditional association rules mining only takes statistical property of data into consideration that means these algorithms ignore the significance of users' context-aware information and users' preference for the items which have an important impact on the association rules we got. This paper pays attention to an improved algorithm on context-factors and preference.