中文摘要
作者共引用分析 (Author Co-citation Analysis，ACA) 常被使用在將作者分至不同領域中，然而傳統的ACA方法卻有如下幾項缺點：
1. 分析時，只選取參考文獻中的第一位作者進入分析中，往往忽略了同一篇中其它優秀的作者。
2. 分群後的結果顯示，一位作者只能歸屬至同一群中，當作者為跨領域或多專長的作者時，則無法以分群結果自然呈現出作者特性。
針對上述兩項缺點，本研究中所提出的方法，主要將參考文獻中的所有作者納入分析，使用完整的作者群 (Complete set and compound set) 取代傳統上僅使用第一位作者為分析單位，經由實驗後所得的分群結果顯示，本研究所提出的方法，可以成功地將作者分至不同群中，亦即一位作者可被同時歸屬至不同的分群中。
此外，本研究尚加入門檻值的設定，以過濾被引用次數較少的作者，即進入分析的作者其被引用的頻率必須大於門檻值，否則將不予納入分析，此舉是為了增加演算法的效率。在門檻值的應用下，將本研究所提出的方法分成兩種方法，一為 Complete author pair，僅在共引用作者群成對時設置門檻值，二為Compound author pair，先針對個別作者被引用的頻率設置一門檻值，當共引用作者群成對時，再設置第二次門檻值。經由實驗結果顯示，當門檻值設定較低時，我們可以納入更多的作者進入分析，而演算法的執行時間也受到門檻值所影響，執行時間長短可經由門檻值的設定而調整，當使用較高門檻值時，則所需的執行時間較短。
最後，本研究所提出的方法具備有如下的優點：
1. 可成功地將一作者分至不同的群中。
2. 使用完整的作者群，分析時可納入較多的作者，同時可避免一些優秀的作者被忽略。
3. 藉由門檻值的設定，使得分析上較為彈性，當需分析較多作者時，可設定較低的門檻值，當考慮演算法的執行時間時，可設定較高的門檻值。

Abstract
Author co-citation analysis (ACA) is a method for identifying relationships between co-cited authors. According to the cited frequency of authors by source papers, ACA method can group authors into different research fields. However, the traditional ACA method may have two drawbacks. First, most of ACA researches include only the first author. Second, an author can be grouped into only one cluster in traditional ACA process.
In this research, we use complete and compound sets instead of only first author to compute author co-relations. As a result two algorithms, namely complete author pair and compound author pair, are proposed. The result shows that our proposed method can group author into multiple clusters successfully and include authors who rarely addressed as the first author. Thresholds in the proposed algorithms can be used to tune performances while reducing authors whose works are rarely collected in the databases.

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