Advances in Knowledge Discovery and Data Mining: 7th - download pdf or read online

By P. S. Bradley (auth.), Kyu-Young Whang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava (eds.)

ISBN-10: 3540047603

ISBN-13: 9783540047605

ISBN-10: 3540361758

ISBN-13: 9783540361756

The seventh Paci?c Asia convention on wisdom Discovery and information Mining (PAKDD) used to be held from April 30 to could 2, 2003 within the conference and Ex- bition middle (COEX), Seoul, Korea. The PAKDD convention is a huge discussion board for educational researchers and practitioners within the Paci?c Asia zone to percentage unique study effects and improvement reports from di?erent KDD-related components resembling information mining, facts warehousing, computer studying, databases, information, wisdom acquisition and discovery, info visualization, and knowledge-based structures. The convention used to be prepared by way of the complex info know-how examine middle (AITrc) at KAIST and the Statistical study heart for advanced structures (SRCCS) at Seoul nationwide college. It was once backed by way of the Korean Datamining Society (KDMS), the Korea Inf- mation technology Society (KISS), the us Air strength O?ce of Scienti?c study, the Asian O?ce of Aerospace examine & improvement, and KAIST. It was once held with cooperation from ACM’s exact staff on wisdom Dis- very and information Mining (SIGKDD).

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Extra resources for Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003, Seoul, Korea, April 30 – May 2, 2003 Proceedings

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Morgan Kaufmann, San Francisco, CA, 1998. 4. P. S. Bradley, J. Gehrke, R. Ramakrishnan, and R. Srikant. Scaling mining algorithms to large databases. Comm. of the ACM, 45(8):38–43, 2002. 5. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984. 6. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998. 7. I. V. Cadez and P. S. Bradley. Model based population tracking and automatic detection of distribution changes.

For existence pattern α: α → e means a new pattern in which orders of elements in α are ignored, but all elements in α must appear before e. In this case, |W (α → e, s, T )| is the total number of T -sized windows that start from any element in α and include all elements of α and e (orders of elements in α are ignored, but all elements in α must appear before e). 2. For sequential pattern β = a1 , a2 , . . , β → e ≡ a1 , a2 , . . , al , e . 3 Problem Definition The formal definition of our problem is: Given sequence s, target event value e, window size T , two thresholds s0 and c0 , find the complete set of rule r = T LHS −→ e such that supp(r) ≥ s0 and conf (r) ≥ c0 .

Mining results minimum support 5% minimum confidence 20% Time window size(hours) 6 12 24 48 Number of rules 1 8 52 328 The telecommunication event database contains the data of a state over 2 months. There are 2,003,440 events recorded in the database. Out of this number, 412,571 events are of type TR. We specify four time intervals and set the thresholds of support and confident as 5% and 20% respectively. The mining results are given in Table 1. An example of rule is shown in Table 2. This rule means signal f 1 and e 1 together will lead to a trouble report N T N within 24 hours with some probabilities.

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Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003, Seoul, Korea, April 30 – May 2, 2003 Proceedings by P. S. Bradley (auth.), Kyu-Young Whang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava (eds.)


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