For Master Talk 2012/10/31

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For Master Talk 2012/10/31

  1. 1. For Master Talk 2012/10/31 M1 Xu Shengbo 1
  2. 2. There are a lot of papers to read... 2
  3. 3. When we star t to read.... 11 12 110 29 3 8 4 7 6 5 3
  4. 4. 11 12 110 29 3 8 4 7 6 5 4
  5. 5. You will get bored... 11 12 110 29 3 8 4 7 6 5 4
  6. 6. 11 12 110 29 3 8 4 7 6 5 5
  7. 7. Finally, you will get sleepy... 11 12 110 29 3 8 4 7 6 5 5
  8. 8. Ho Iw w to ill Co tal mp k a Ef fic reh bou ien en t tly da !! Pa p er 6
  9. 9. What you’d better to read first is... Abstract Introduction Conclusion In this paper, we present a new definition for An outlier in a dataset is defined informally as In this paper, we present a new definition for outlier: cluster-based local outlier, which is an observation that is considerably different outlier: cluster-based local outlier, which is meaningful and provides importance to the local from the remainders as if it is generated by a intuitive and provides importance to the local data behavior. A measure for identifying the physical different mechanism. Searching for outliers is an data behavior. A measure for identifying the significance of an outlier is designed, which is called cluster-based local outlier factor (CBLOF). We also important area of research in the world of data physical significance of an outlier, namely propose the FindCBLOF algorithm for discovering mining with numerous applications, including CBLOF, is also defined. Furthermore, we outliers. The experimental results show that our credit card fraud detection, discovery of criminal propose the Find- CBLOF algorithm for approach outperformed the existing methods on activities in electronic commerce, weather discovering outliers. The experimental results identifying meaningful and interesting outliers. prediction, marketing and customer show that our approach out- performed existing segmentation. methods on identifying meaningful and interesting outliers. Recently, some studies have been proposed onWe can get the information outlier detection (e.g., Knorr and Ng, 1998; Ramaswamy et al., 2000; Breunig et al., 2000; For future work, we will integrate the Find- CBLOF algorithm more tightly with clustering of the paper roughly. Aggarwal and Yu, 2001) from the data mining community. This paper presents a new definition algorithms to make the detecting process more efficient. The designing of effective top-k for outlier, namely cluster-based local outlier, outliersÕ detection algorithm will be alsoIt is okay, if you cannot which is intuitive and meaningful. This work is motivated by the following observations. addressed. understand details. .... We can learn what we should understand We can understand the from the paper. relationship with previous work, and the procedure of the research. 7
  10. 10. And get additional information from... Figure pseudocode Table Graph 8
  11. 11. Reference...http://www.e.ics.nara-wu.ac.jp/~nogu/tips/ ronbun_yomikata.html 9

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