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Advancements in medical ultrasound systems, challenges both manufacturers and clinicians in finding image functions and parameters that optimize image quality. We propose a machine learning method based on data mining for finding the best data path for certain exam types. Attribute relevance analysis helps us identify weakly relevant image parameters. The searching of frequent itemsets using apriori algorithm offers the best combination of image functions and their associated parameters. A commercially available ultrasound scanner was modified for our data collection, algorithmic verification, and analysis. Test results show that our proposed data mining methods may help manufacturers identify the most useful clinical image functions and help doctors choose right parameters as default settings that increase patient's throughput.