關聯規則探勘(Association Rule Mining)是資料科學中一種常見的研究方法，在資料量龐大的情況下通常有許多隱藏的資訊藏於其中，如果不使用任何工具只透過我們人工的觀察往往得不到任何重點結果，而透過關聯規則探勘演算法的幫助之下我們可以找出許多意想不到的組合。因此關聯規則探勘被廣泛的應用在不同領域上，最常見的例子便是顧客購買商品時經常會遇到的「購物籃分析」(Basket Analysis) 問題，然而現有的關聯規則探勘研究幾乎很少應用在網路科學的層面上，在點跟線所組成的網路中我們期望可以透過關聯規則探勘來解決一些網路科學裡經常會遇到的問題，也希望可以透過這個方式發現意想不到的結果。
我們研究的對象是專利引用網路(Patent Citation Network)，在此網路中我們把專利間的引用關係視為人與商品之間的購買問題，透過關聯規則探勘的分析我們預期可以發現許多規則組合並且透過這些結果更進一步的驗證關聯規則探勘對於專利網路亦或是網路科學的實用性以及有效性。首先我們使用Apriori演算法找出頻繁項目集，接著根據結果使用關聯規則探勘得出關聯規則，最後我們使用這些不同的關聯規則進行各方面評估。
實驗結果我們發現在專利引用網路中使用關聯規則探勘發現的關聯規則是極具有意義的，我們可以將這些規則當作是一個推薦系統，並且這種推薦方式非常可靠且準確，不同於透過關鍵訊息進行專利推薦或搜尋的系統，我們是透過一個個專利引用不斷地推薦下一個可引用的專利，換句話說使用者每引用一篇專利我們都可以推薦使用者下一篇相關或相似的專利，從而達到引用或是參考等等目的。同時我們還發現透過對資料集矩陣進行轉置後再計算關聯規則，可以透過規則發現結構性相似亦或是內容相似的專利組合，使用者可以輕鬆找出與指定專利在某種程度上相似的另一篇專利，從而參考使用。

Association Rule Mining is a common research method in data science. In cases with large amounts of data, there are often hidden information and insights that cannot be easily obtained through manual observation alone. By utilizing association rule mining algorithms, we can discover unexpected combinations and patterns. As a result, association rule mining is widely applied in various fields. One common example is ”basket analysis” in customer purchasing behavior, where associations between items in shopping baskets are explored. However, there is limited research on applying association rule mining in the context of network science. In networks composed of nodes and edges, we aim to solve problems frequently encountered in network science through association rule mining, hoping to uncover unexpected findings.
Our research focuses on the Patent Citation Network, where we treat the citation relationships between patents as purchase transactions between people and items. Through the analysis of association rules, we expect to discover various rule combinations. Furthermore, we aim to validate the utility and effectiveness of association rule mining in the context of patent networks and network science. We start by using the Apriori algorithm to identify frequent itemsets. Then, based on the results, we apply association rule mining to derive association rules. Finally, we evaluate the obtained rules from different perspectives.
The experimental results reveal that association rules discovered through association rule mining in the Patent Citation Network are highly meaningful. We can treat these rules as a recommendation system, which is reliable and accurate. Unlike patent recommendations or search systems based on keyword information, our approach continuously recommends the next citable patent based on individual patent citations. In other words, for each cited patent, we can recommend the user another relevant or similar patent, achieving the goal of citation or reference. Additionally, we discovered that by transposing the dataset matrix and calculating association rules, we can identify structurally or content-wise similar combinations of patents. Users can easily find another patent that is somewhat similar to a specified patent, facilitating their referencing process.

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