近年來網路拍賣參與人數與成交量不斷成長，然而網路匿名性無法確認出價者身分與歷史出價紀錄，也衍生出許多網路詐欺問題，像是惡意出價者以不當行為出價，試圖哄抬商品價格。
本研究依據文獻探討整理出7個出價行為變數，(1)出價者參與同一位賣家拍賣之比率；(2)出價次數；(3)得標次數；(4)出價時間；(5)出價增額；(6)進入拍賣的時間；(7)離開拍賣的時間。隨著近年來網路的匿名性使得價格哄抬更難以被發現，相較過去研究大多使用具有明碼資訊的研究資料，未曾探討網路匿名性的問題，故本研究認為如何辨識隱碼出價者的身分變成首要的課題，才能進一步分析出價者的行為，本研究利用出價者的資訊，提出辨識隱碼出價者身分的方法，透過評價增加幅度、出價行為變數分區相同隱碼的出價者，並利用明碼出價者進行驗證，確認分群方式是顯著的。
最後根據出價行為將所有出價者分為5群：(1)估價者；(2)嚇阻者；(3)投機者；(4)可疑者；(5)參與者，整理出五大出價集群的行為特性，並提出價格哄抬機率公式，作為偵測可以出價者的指標。期望能提供賣家或拍賣平台了解其出價行為，針對特定的集群給予相對應的管理機制，並能即時預防價格哄抬者，保護其他出價者的權益。

In recent years, trading volume of online auction has been climbing steadily. However, the anonymous scheme hides confirm bidder’s identity and bidding history. This may lead to frauds, such as shill bidders participate in the process with the no intention to win but to raise competitive prices.
We reviewed existing literature to find 7 bidder’s behavior variables that may be used to identify potential shill bidders: (1) frequency of a bidder participated in the same seller, (2) number of bids, (3) number of winning bids, (4) inter-bid time, (5) inter-bid increment, (6) timing of the first bid, (7) timing of the last bid. We used anonymous data to investigate whether shill bidders can be identified by their bidding behavior.
In this research, we have developed a clustering-based approach that uses the increment of reputation and 7 behavior variables of the bidder to determine the probability of shill bidding. A dataset that includes both anonymous bidders and known bidders is used to evaluate the method. Five clusters have been identified from the anonymous data. The likelihood of shill bidding in each cluster was assessed. The data subset of winning bidders was used to evaluate the accuracy of the clustering model. The result indicates that our clustering-based approach can effectively assess the probability of a shill bidder from their bidding behavior. The contribution of this research allows shill bidders to be identified in the bidding process.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
圖目錄 vii
表目錄 viii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 3
第三節 研究流程 5
第二章 文獻探討 6
第一節 拍賣定義與模式 6
第二節 網路拍賣 9
第三節 主成分分析 19
第四節 機器學習 20
第三章 研究方法 28
第一節 導論 28
第二節 分群演算法 28
第三節 研究資料 31
第四節 出價行為變數 32
第五節 辨識隱碼出價者身分 42
第四章 研究結果與分析 46
第一節 辨識隱碼出價者身分 46
第二節 價格哄抬機率公式 55
第三節 出價者分群與驗證 61
第四節 集群行為分析 64
第五章 結論與建議 69
第一節 研究結論 69
第二節 研究限制與建議 71
參考文獻 72
中文文獻 72
英文文獻 73
附錄 77
附錄一 研究資料 77

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