Data quality is a perennial problem for many enterprise data assets. To improve data quality, businesses often employ rule based data standardization systems in which domain experts code rules for handling important and prevalent patterns. Finding these patterns is laborious and time consuming, particularly for noisy or highly specialized data sets. It is also subjective to the persons determining these patterns. In this paper we present a tool to automatically mine patterns that can help in improving the efficiency and effectiveness of these data standardization systems. The automatically extracted patterns are used by the domain and knowledge experts for rule writing. We use a greedy algorithm to extract patterns that result in a maximal coverage of data. We further group the extracted patterns such that each group represents patterns that capture similar domain knowledge. We propose a similarity measure that uses input pattern semantics to group these patterns. We demonstrate the effectiveness of our method for standardization tasks on three real world datasets. © 2013 IEEE.