在醫療領域，患者通過電子健康記錄（EHR）生成大量的健康記錄。提取患者病理狀況或診斷的有用知識對於基於規則的系統中的推理過程至關重要，以支持臨床決策過程。已知關聯規則挖掘能夠發現隱藏的有趣知識以及數據集（包括醫學數據集）中屬性之間的關係。關於關聯規則挖掘的大多數研究都集中在根據數據集中出現的最頻繁項發現頻繁模式。但是，提取稀有規則的過程很少受到關注。在醫學數據集研究中，稀有關聯規則（RAR）的發現更具挑戰性，因為它很可能會用於為醫生獲取更多潛在的稀有和不尋常的知識。而且，ARM更可能產生許多異常規則，這會導致邏輯錯誤並影響基於規則的系統的推理過程。因此，挑戰在於開發一種方法來提取簡潔的稀有規則庫，消除稀有模式中的異常規則以及從數據集中找到最全面的規則。
在這項研究中，我們通過幾個步驟從腦血管檢查數據集生成非異常稀有關聯規則（NA-RAR）：獲取頻繁的封閉項目集，生成關聯規則庫，進行異常檢查以適合規則中的生產規則（PR）。基於系統。實驗表明，與傳統方法相比，該方法消除了許多異常規則，同時縮短了計算時間。此外，它可以發現一組非異常稀有關聯規則（NA-RAR）和類稀有關聯規則，以提供潛在的有意義的知識來支持用戶域的臨床決策過程。

In medical field, patients generate massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients’ pathological conditions or diagnoses is essential for reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining is known capable to discover hidden interesting knowledge and relationships among attributes in dataset, including medical dataset. Most research on association rule mining focuses on discovering frequent patterns based on the most frequent items occurring in the dataset. However, the process of extracting rare rules has received less attention. In medical dataset studies, the discovery of rare association rules (RARs) is more challenging, because it could likely be used to obtain more potentially rare and unusual knowledge for physicians. Moreover, ARM more likely to produce many anomalous rules, which lead to logical errors and affect the reasoning process of rule-based systems. Therefore, the challenge is to develop a method to extract concise rare rule-bases, to eliminate anomalous rule in rare patterns, and also to find the most comprehensive rules from the dataset.
In this study, we generate non-anomalous rare association rules (NA-RARs) from cerebrovascular examination dataset through several steps: obtaining infrequent closed itemset, generating rare association rules-bases, anomalies checking, to fit production rules (PRs) in rule-based systems. Experiment showed that the proposed method eliminated a significant number of anomalous rules while improving computational time, compared with the traditional method. Moreover, it could discover a set of non-anomalous rare association rules (NA-RARs) and class rare association rules to provide potential meaningful knowledge to support the process of clinical decision making by user domain.

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