Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usual...Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usually high-dimensional and sparse. Two approaches for mining typical user profiles, based on matrix dimensionality reduction, are presented. In these approaches, non-negative matrix factorization is applied to reduce dimensionality of the session-URL matrix, and the projecting vectors of the user-session vectors are clustered into typical user-session profiles using the spherical k -means algorithm. The results show that two algorithms are successful in mining many typical user profiles in the user sessions.展开更多
Algorithms for mining quantitative association rules consider each attribute equally, but the attributes usu-ally have different importance. Two kinds of algorithms for mining the weighted fuzzy association rules are ...Algorithms for mining quantitative association rules consider each attribute equally, but the attributes usu-ally have different importance. Two kinds of algorithms for mining the weighted fuzzy association rules are providedwith respect to two kinds of database. The first algorithm can effectively consider the importance of quantitative at-tributes, and considers that the importance of association rule is not increased with the amount of attributes in therule. The second algorithm not only considers the importance of quantitative attributes, but also considers that theimportance of association rule is increased with the amount of attributes in the rule.展开更多
文摘Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usually high-dimensional and sparse. Two approaches for mining typical user profiles, based on matrix dimensionality reduction, are presented. In these approaches, non-negative matrix factorization is applied to reduce dimensionality of the session-URL matrix, and the projecting vectors of the user-session vectors are clustered into typical user-session profiles using the spherical k -means algorithm. The results show that two algorithms are successful in mining many typical user profiles in the user sessions.
文摘Algorithms for mining quantitative association rules consider each attribute equally, but the attributes usu-ally have different importance. Two kinds of algorithms for mining the weighted fuzzy association rules are providedwith respect to two kinds of database. The first algorithm can effectively consider the importance of quantitative at-tributes, and considers that the importance of association rule is not increased with the amount of attributes in therule. The second algorithm not only considers the importance of quantitative attributes, but also considers that theimportance of association rule is increased with the amount of attributes in the rule.
