With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role for ensuring the overall quality of software systems. Alas, data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues, data pattern violations, we observe that these studies usually require data-specific configurations (i.e., parameterized) or a certain set of fully curated data as learning examples (i.e., supervised). Both approaches require domain knowledge and depend on users’ deep understanding of their data, and are often effort-intensive. In this paper, we introduce RIOLU: Regex Inferencer autO-parameterized Learning with Uncleaned data. RIOLU is fully automated, is automatically parameterized, and does not need labeled samples. We observe that RIOLU can generate precise patterns from datasets in various domains, with a high F1 score of 97.2%, exceeding the state-of-the-art baseline. In addition, according to our experiment on five datasets with anomalies, RIOLU can automatically estimate a data column’s error rate, draw normal patterns, and predict anomalies from unlabeled data with higher performance (up to 800.4% improvement in terms of F1) than the state-of-the-art baseline. Furthermore, RIOLU can even outperform ChatGPT in terms of both accuracy (12.3% higher F1) and efficiency (10% less inference time). With user involvement, a variation (a guided version) of RIOLU can further boost its precision (up to 37.4% improvement in terms of F1). Our evaluation in an industrial setting further demonstrates the practical benefits of RIOLU.