Selecting the right interestingness measure for association patterns

@misc{citeulike:2366533, abstract = {Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as support, confidence, lift, correlation, and collective strength are often used to determine the interestinghess of association patterns. However, many such measures provide conflicting information about the interestinghess of a pattern, and the best metric to use for a given application domain is rarely known. In this ...}, author = {Tan, P. and Kumar, V. and Srivastava, J. }, citeulike-article-id = {2366533}, keywords = {association, interestingness, measures, rules}, posted-at = {2008-02-12 16:32:36}, priority = {3}, title = {Selecting the right interestingness measure for association patterns}, url = {http://citeseer.ist.psu.edu/tan02selecting.html}, year = {2002} }

TY - GEN ID - citeulike:2366533 L3 - citeulike-article-id:2366533 TI - Selecting the right interestingness measure for association patterns N2 - Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as support, confidence, lift, correlation, and collective strength are often used to determine the interestinghess of association patterns. However, many such measures provide conflicting information about the interestinghess of a pattern, and the best metric to use for a given application domain is rarely known. In this ... KW - association KW - interestingness KW - measures KW - rules AU - Tan, P AU - Kumar, V AU - Srivastava, J PY - 2002/// UR - http://citeseer.ist.psu.edu/tan02selecting.html ER -