For Master Talk2012/10/31

M1 Xu Shengbo

1

There are a lot of papers to read...

2

When we star t to read....12

39

6

12

457

8

1011

3

12

39

6

12

457

8

1011

4

You will get bored...12

39

6

12

457

8

1011

4

12

39

6

12

457

8

1011

5

Finally, you will get sleepy...12

39

6

12

457

8

1011

5

I will talk about

How to Comprehend a Paper

Efficiently!!

6

What you’d better to read first is...

Introduction An outlier in a dataset is defined informally as an observation that is considerably different from the remainders as if it is generated by a different mechanism. Searching for outliers is an important area of research in the world of data mining with numerous applications, including credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction, marketing and customer segmentation.

Recently, some studies have been proposed on outlier detection (e.g., Knorr and Ng, 1998; Ramaswamy et al., 2000; Breunig et al., 2000; Aggarwal and Yu, 2001) from the data mining community. This paper presents a new definition for outlier, namely cluster-based local outlier, which is intuitive and meaningful. This work is motivated by the following observations.

....

We can understand the relationship with previous work, and the procedure

of the research.

Abstract In this paper, we present a new definition for outlier: cluster-based local outlier, which is meaningful and provides importance to the local data behavior. A measure for identifying the physical significance of an outlier is designed, which is called cluster-based local outlier factor (CBLOF). We also propose the FindCBLOF algorithm for discovering outliers. The experimental results show that our approach outperformed the existing methods on identifying meaningful and interesting outliers.

We can get the information of the paper roughly.

It is okay, if you cannot understand details.

Conclusion In this paper, we present a new definition for outlier: cluster-based local outlier, which is intuitive and provides importance to the local data behavior. A measure for identifying the physical significance of an outlier, namely CBLOF, is also defined. Furthermore, we propose the Find- CBLOF algorithm for discovering outliers. The experimental results show that our approach out- performed existing methods on identifying meaningful and interesting outliers.

For future work, we will integrate the Find- CBLOF algorithm more tightly with clustering algorithms to make the detecting process more efficient. The designing of effective top-k outliersÕ detection algorithm will be also addressed.

We can learn what we should understand

from the paper.

7

And get additional information from...

Figurepseudocode

Graph

Table

8

Reference...

http://www.e.ics.nara-wu.ac.jp/~nogu/tips/ronbun_yomikata.html

9