在一個資料庫裡，異常是指特性相異於其他資料的資料點。偵測出這些異常在許多應用中是很重要的。大部分傳統異常探勘的資料屬性都著重在數值屬性，但是現實生活中有很多類別屬性的資料，所以傳統的異常探勘方法就不適合使用。FindFPOF (frequent pattern outlier factor)演算法是利用頻繁項目集找出異常交易，但是FindFPOF會產生出許多頻繁樣式集。我們的目的是找出異常而不是在找出頻繁樣式，因此，本論文提出減少頻繁項目集(reduce frequent pattern set ; Rfp)來找出異常交易的方法，稱之為RfpFPOF。實驗結果顯示本論文所提出的方法在類別資料庫中發現異常交易的效能優於FindFPOF。

An outlier in a database is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Outlier detection is very import for many applications. Most existing methods are designed for numeric data. They will have problems with real-life applications that contain categorical data. In order to solve this problem, some scholars propose the FindFPOF(Frequent Pattern Outlier Factor) algorithm to detect outliers from discovered frequent patterns. However, FindFPOF generates too many frequent patterns. The purpose of this study is to identify outlier not to find frequent patterns. Therefore, in this study, we present an efficient method to detect outliers by reducing the number of frequent patterns, called the reduce frequent pattern FPOF (RfpFPOF) algorithm. The experimental results have shown that RfpFPOF outperformed the FindFPOF method on identifying outliers execution time.

摘 要 I
ABSTRACT II
誌 謝 III
目錄 IV
圖目錄 V
表目錄 VI
一、緒論 1
1.1研究背景 1
1.2 研究動機與目的 2
1.3 論文架構 2
二、文獻探討 3
2.1 異常探勘 3
2.2 關聯法則探勘 5
2.3頻繁樣式基礎之離異探勘 9
三、以頻繁樣式為基礎之異常交易偵測演算法 13
3.1方法概念 13
3.2演算法分析 16
四、實驗結果與效能分析 19
4.1實驗環境與評估方式 19
4.2實驗資料集 19
4.3效能評估 20
五、結論與未來研究方向 25
5.1 結論 25
5.2 未來研究方向 25
參考文獻 26

[1]C.C. Aggarwal and P.S. Yu, “Outlier Detection for High Dimensional Data,” Proc. ACM-SIGMOD 2001 Int’l. Conf. on Management of Data, pp.37-46, May 2001.
[2]C.C. Aggarwal and P.S. Yu, “An Effective and Efficient Algorithm for High-Dimensional Outlier Detection,” The VLDB Journal, vol.14, no.2, pp.211-221, 2005.
[3]D. Agrawal and C.C. Aggarwal, “On the Design and Quantification of Privacy Preserving Data Mining Algorithms,” Proc. 20th ACM SIGACT-SIGMOD-SIGART Symposium on PODS, pp.247-255, May 2001.
[4]R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” Proc. ACM-SIGMOD 1993 Int’l. Conf. on Management of Data, pp.207-216, May 1993.
[5]V. Barnett and T. Lewis. Outliers in Statistical Data. John Whiley, Sons and Chichester, 1994.
[6]M.M. Breunig, H.P. Kriegel, R.T. Ng, and J. Sander, “LOF: Identifying Density-based Local Outliers,” Proc. ACM-SIGMOD 2000 Int’l. Conf. on Management of Data, pp.93-104, May 2000.
[7]M.S. Chen, J. Han, and P.S. Yu, “Data Mining: An Overview from Database Perspective,” IEEE Trans. Knowledge and Data Eng., vol.8, no.6, pp.866-883, 1996.
[8]J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
[9]S. Harkins, H. He, G.J. Williams, and R.A. Baxter, “Outlier Detection Using Replicator Neural Networks,” Proc. 4th Int’l. Conf. DaWaK 2002, pp.170-180, Sep. 2002.
[10]Z. He, X. Xu, and S. Deng, “Discovering Cluster-based Local Outliers,” Pattern Recognition Letters, vol.24, no.9-10, pp.1641-1650, 2003.
[11]Z. He, X. Xu, J.Z. Huang, and S. Demg, “A Frequent Pattern Discovery Method for Outlier Detection,” Proc. 5th Int’l. Conf. WAIM 2004, pp.726-732, July 2004.
[12]Z. He, X. Xu, J.Z. Huang, and S. Deng, “FP-Outlier: Frequent Pattern Based Outlier Detection,” Computer Science and Information System, vol.2, no.1, 2005.
[13]Z. He, X. Xu, and S.Deng, “A Unified Subspase Outlier Ensemble Framework for Outlier Detection in High Dimensional Spaces,” Proc. 6th Int’l Conf. Web-Age Information Management, pp.632-637, Oct. 2005.
[14]M.F. Jiang, S.S. Tseng, and C.M. Su, “Two-Phase Clustering Process for Outliers Detection,” Pattern Recognition Letters, vol.22, no.6-7, pp.691-700, 2001.
[15]E.M. Knorr and R.T. Ng, “Algorithms for Mining Distance-based Outliers in Large Data Sets,” Proc. 24th Int’l. Conf. on Very Large Data Bases, pp.392-403, Sep. 1998.
[16]E.M. Knorr, R.T. Ng, and V. Tucakov, “Distance-based Outliers: Algorithms and Applications,” The VLDB Journal, vol.8, no.3-4, pp. 237–253, 2000.
[17]M.E. Otey, A. Ghoting, and A. Parthasarathy, “Fast Distributed Outlier Detection in Mixed-Attribute Data Sets,” Data Mining and Knowledge Discovery, vol.12, no.2-3, pp.203-228, 2006.
[18]S. Ramaswamy, R. Rastogi, and S. Kyuseok, “Efficient Algorithms for Mining Outliers from Large Data Sets,” Proc. ACM-SIGMOD 2000 Int’l. Conf. on Management of Data, pp.93-104, May 2000.
[19]G.J. Willams, R.A. Baxter, H. He, S. Hawkins, and L. Gu, “A Comparative Study of RNN for Outlier Detection in Data Mining,” Proc. IEEE ICDM 2002, pp.709-712, Dec. 2002.
[20]L. Wei, W. Qian, A. Zhou, W. Jin, and J.X. Yu, “HOT: Hypergraph-based Outlier Test for Categorical Data,” Proc. 7th Pacific-Asia Conf. PAKDD 2003, pp.399-410, April-May 2003.
[21]D. Yu, G. Sheikholeslami, and A. Zang, “Findout: Finding Outliers in Very Large Datasets,” Knowledge and Information Systems, vol.4, no.3, pp. 387-412, 2002.