Associative classification algorithm and its existing improved algorithms cannot achieve both high overall accuracy and good minority class classification. To solve this problem, we propose an improved associative classification algorithm based on various class-support thresholds (ACCS)independent mining rules. Its main featuresare: (1) ACCS sets the support threshold of each class according to the class size in the training data, and extracts the associative classification rule of each class separately based on the class-support threshold in order to get higher confidence rules of minority classes; (2) ACCS uses the class-support threshold to rank the rules with the same confidence for increasing the priority of the minority classes; (3) ACCS combines confidence and lift degrees together to predict unknown instances. The experimental results on multiple datasets show that ACCS can achieve higher overall classification accuracy than the existing associative algorithms, and can also get good minority class classification performance in imbalanced data.