臺灣博碩士論文加值系統

內政部警政署刑事警察局為提升刑事工作的效能，根據刑事偵查人員的辦案需求與特性建置刑案知識庫。刑案知識庫蒐集犯罪嫌疑人的相關資料，例如身分證號碼、姓名、車號、案由及案件編號等等。目前刑案知識庫已成為支援輔助刑事偵查人員最重要的工具之一。另外，為因應內部管理需要與統計分析需求，刑案知識庫也記錄使用者的查詢軌跡，例如使用者代號、案件編號、查詢時間、查詢項目及查詢結果等等。本研究探索分析刑案知識庫的查詢軌跡資料，從中挖掘萃取刑事偵查人員的偵查智慧，探勘犯罪偵查資料的關聯，進而研擬刑案知識庫查詢推薦技術，以持續精進刑案知識庫的查詢服務功能。
本研究研擬三個刑案知識庫查詢推薦演算法，包括項目推薦演算法、最近共現鄰居推薦演算法、以及項目屬性推薦演算法。項目推薦演算法以測試資料集案件的查詢項目，找出訓練資料集案件中查詢項目相符的案件，據以找出推薦項目。最近共現鄰居推薦演算法，以項目推薦演算法為基礎，再輔以項目的案件共現關係矩陣，把推薦項目的最近共現鄰居也作為推薦項目。項目屬性推薦演算法，雖也以項目推薦演算法為基礎，但是僅以項目的(案類)屬性推薦查詢方向。
本研究蒐集「刑案知識庫」內2013年至2016年新北市政府警察局共4,793件刑案的查詢軌跡作為實驗資料集。實驗資料集以隨機方式抽選其中的10分之7作為訓練資料集，其餘的10分之3作為測試資料集。並取十次實驗的平均作為實驗結果。實驗結果顯示，項目推薦演算法的推薦成功率平均為4.67%，而比較基準隨機推薦的推薦成功率平均僅為0.02%，推薦成功率約可提升233倍。此外實驗結果也顯示最近共現鄰居推薦演算法的推薦成功率平均為5.13%，又較項目推薦演算法大幅提升9.85%。另外項目屬性推薦演算法的推薦成功率平均為24.58%，而比較基準隨機推薦的推薦成功率平均僅為22.41%，推薦成功率提升9.68%。綜合而言，透過刑案知識庫查詢推薦技術，提供使用者有別於傳統關聯式查詢的服務，可進一步精進刑案知識庫的查詢服務功能。
關鍵詞：資料探勘，偵查智慧，推薦技術，刑案知識庫，查詢軌跡

In order to enhance the effectiveness of criminal investigation, the Criminal Investigation Bureau of the National Police Agency under the Ministry of the Interior has built a Criminal Case Knowledge Base to meet the needs and characteristics of criminal investigators. The Criminal Case Knowledge Base collects relevant data on suspects, such as their identification card numbers, names, license plate numbers, offenses charged and case numbers. So far, the Criminal Case Knowledge Base has become one of the most important tools for supporting and assisting criminal investigators. In addition, in response to the requirements of internal management and statistical analysis, the Criminal Case Knowledge Base also records a user’s searching trajectory, such as user code, case number, searching time, items searched (also called search-items) and searching results. This research explores and analyzes the data on searching trajectories in the Criminal Case Knowledge Base. The relationships among investigative searching trajectories, which might implicitly contain the investigative intelligence of criminal investigators, are extracted by using data mining techniques. This information is then used to develop the recommendation algorithms for the Criminal Case Knowledge Base.
This research developed three recommended algorithms for the Criminal Case Knowledge Base including an item-based recommendation algorithm, a co-occurrence nearest neighbor-based recommendation algorithm, and an item attribute-based recommendation algorithm. The item-based recommendation algorithm uses a search-item in the test dataset to find matched search-item(s) in the training dataset. The search-item(s) following the matched search-item(s) are then used as the recommendation items for the search-item (in the test dataset). In addition to the recommendation items generated by the item-based recommendation algorithm, the co-occurrence nearest neighbor-based recommendation algorithm includes the co-occurrence nearest neighbors of the recommendation items in the recommendation item set. Although the item attribute-based recommendation algorithm is also based on the item-based recommendation algorithm, it recommends the search direction(s) for the search-item instead.
This research collected searching trajectories of 4,793 criminal cases between 2013 and 2016 from the Criminal Case Knowledge Base of the New Taipei City Police Department as its experiment dataset. Seventy percent of the experiment dataset was randomly selected and used as the training dataset, while the other thirty percent was used as the test dataset. The average value of 10 experiments was used as the experiment result. The experiment results show that the average successful recommendation rate of the item-based recommendation algorithm is 4.67%, while the average successful recommendation rate of random recommendation is only 0.02%. Compared with the latter, the successful recommendation rate of the former increases approximately 233 folds. In addition, the experiment results suggest that the average successful recommendation rate of the co-occurrence nearest neighbor-based recommendation algorithm is 5.13%, which is 9.85% growth from the item-based recommendation algorithm. Moreover, the average successful recommendation rate of the item attribute-based recommendation algorithm is 24.58%, while the average successful recommendation rate of random recommendation is only 22.41%. Compared with the latter, the successful rate of the former increases by 9.68%. In summary, the recommendation algorithms proposed in this research can be used to improve the efficiency of the investigative searching in the Criminal Case Knowledge Base.
Keywords: Data Mining, Investigative Intelligence, Recommendation Techniques, Criminal Case Knowledge Base, Searching Trajectory.

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、研究範圍與限制 5
第四節、章節架構 6
第二章、文獻探討 7
第一節、犯罪偵查 7
第二節、刑案知識庫 8
一、發展沿革 8
二、查詢功能 9
三、查詢軌跡概況 10
四、查詢軌跡內容 11
第三節、網站探勘 13
第四節、關聯規則分析 13
第五節、案例式推理 14
第六節、概念階層 15
第七節、K-最近鄰演算法 16
第八節、推薦技術 16
第九節、推薦效能評估 18
一、離線實驗 18
二、用戶調查 19
三、線上實驗 19
第三章、研究方法與實驗環境 20
第一節、查詢軌跡簡化 20
第二節、項目推薦演算法 21
第三節、基於最近共現鄰居的推薦方法 26
一、最近共現鄰居 26
二、共現關係矩陣 26
三、最近共現鄰居推薦演算法 27
第四節、項目屬性推薦演算法 28
第五節、推薦成功率評估之比較基準 31
一、推薦成功率計算方式 31
二、推薦成功率比較基準 32
第六節、實驗環境 33
第四章、實驗流程與結果 34
第一節、資料前處理 34
一、查詢軌跡內容格式 34
二、查詢軌跡去個資化 34
三、新增項目順序 35
四、判斷僅一項及最後一項 35
五、產生測試資料集與訓練資料集 36
第二節、實驗結果 36
一、項目推薦演算法實驗結果 37
二、最近共現鄰居推薦演算法實驗結果 37
三、項目屬性推薦演算法實驗結果 38
第五章、結論 39
第一節、研究成果 39
第二節、未來研究方向 39
中英文參考文獻 40

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