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The approach presented in this paper is to discover association rules based on a user's query. Of the many issues in rule discovery, relevancy, interestingness, and supportiveness of association rules are considered in this paper. For a given user query, a database can be partitioned into three views: a positively-related-query view, a negatively-related-query view, and an unrelated-query view. We present a methodology for data mining and rule discovery that incorporates pattern extraction from the three types of query views with pattern spanning to enlarge the scope of a pattern and its derived association rule. The rule discovery process involves several interrelated steps: 1) pattern extraction from both positively- and/or negatively-related query views, 2) pattern association across attributes to enhance the semantics of patterns, while performing 3) pattern spanning within an attribute domain to enhance the supportiveness of the resulting pattern. The contributions of the paper includes the specification and development of the data mining method and associated tool that combines the operations of association and spanning on query views to derive semantically interesting patterns. These patterns can then be used in decision making because the patterns were mined from the user's original hypothesis as expressed by the query.