In this paper, we explore extending association analysis to non-traditional types of patterns and non-binary data by generalizing the notion of confidence. The key idea is to regard confidence as a measure of the extent to which the strength of one association pattern provides information about the strength of another. This approach provides a framework that encompasses the traditional concept of confidence as a special case and can be used as the basis for designing a variety of new confidence measures. Besides discussing such confidence measures, we provide examples that illustrate the potential usefulness of a generalized notion of confidence. In particular, we describe an approach to defining confidence for error tolerant itemsets that preserves the interpretation of confidence as a conditional probability and derive a confidence measure for continuous data that agrees with the standard confidence measure when applied to binary transaction data.