Attribute Oriented Induction High level Emerging Pattern AOI-HEP has been proven as data mining algorithm which can mine frequent and similar patterns. However, the current finding AOI-HEP patterns cannot be measured in term of confidence the finding AOI-HEP patterns either for each AOI-HEP pattern or dataset. The exploration of confidence AOI-HEP patterns will be explored in order to give confidence mining pattern for each AOI-HEP pattern either frequent or similar pattern, and each dataset as confidence AOI-HEP pattern between frequent and similar patterns. Confidence per AOI-HEP pattern will show how interested each of AOI-HEP pattern, whilst confidende per dataset will show how interested each dataset between frequent and similar patterns. Confidence of each AOI-HEP pattern will be scored with equations 1 and 2 whilst confidence of AOI-HEP pattern in each dataset will be scored with equation 5. The experiments for finding confidence of each AOI-HEP pattern showed that AOI-HEP pattern with growthrate under and above 1 will be recognized as uninterested and interested AOI-HEP mining pattern since having confidence AOI-HEP mining pattern under and above 50% respectively. Furthermore, the uniterested AOI-HEP mining pattern which usually found in AOI-HEP similar pattern, can be switched to interested AOI-HEP mining pattern by switching their support positive and negative value scores. The example of switching from uninterested to interested changed their growthrate score as shown in table 8 number 2 and table 9. Finally, the experiments for finding confidence of AOI-HEP pattern in each dataset showed that IPUMS datasets have similar confidence 100% mining AOI-HEP frequent pattern and 0% mining AOI-HEP similar pattern. Meanwhile, breast cancer dataset has equally confidence 50% for mining both AOI-HEP frequent and similar patterns, and census dataset is equally confidence none or 0% for mining both AOI-HEP frequent and similar patterns.