Advanced Data Mining and Applications: Third International by Zhi-Hua Zhou (auth.), Reda Alhajj, Hong Gao, Xue Li,

By Zhi-Hua Zhou (auth.), Reda Alhajj, Hong Gao, Xue Li, Jianzhong Li, Osmar R. Zaïane (eds.)

The 3rd foreign convention on complex information Mining and purposes (ADMA) prepared in Harbin, China persisted the culture already proven by means of the 1st ADMA meetings in Wuhan in 2005 and Xi’an in 2006. One significant objective of ADMA is to create a good id within the facts mining study com- nity. This feat has been in part completed in a truly short while regardless of the younger age of the convention, due to the rigorous overview procedure insisted upon, the phenomenal checklist of the world over well known keynote audio system and the superb application every year. The influence of a convention is measured via the citations the convention papers obtain. a few have used this degree to rank meetings. for instance, the autonomous resource ranks ADMA (0.65) larger than PAKDD (0.64) and PKDD (0.62) as of June 2007, that are good proven meetings in facts mining. whereas the rating itself is questionable as the unique strategy isn't really disclosed, it really is however an encouraging indicator of popularity for a really younger convention comparable to ADMA.

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Extra info for Advanced Data Mining and Applications: Third International Conference, ADMA 2007 Harbin, China, August 6-8, 2007. Proceedings

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In [30], a hybrid scheme based on fuzzy sets and rough sets are proposed for breast cancer detection. Fuzzy sets are firstly used to pre-processing breast cancer images for enhancing the contrast of images. Rough sets-based approaches are applied for attribute reduction and rule extraction. Experimental results show that the hybrid scheme performs well reaching over 98% in overall accuracy. In [31] fuzzy sets are 40 R. Li et al. introduced to a rough sets-based information measure for getting the reducts with a better performance.

There are several possible ways to mitigate this compatibility problem. One approach is to balance the scores between the two sets. In our experiments with MDP and QCS, we accomplished this by dividing each score in each candidate set by the mean of the top ten scores in the set. We consider two additional options that allow further and fine-grained integration of the page scores. The first is the weighted ordinal method, where pages in each set are sorted by their cosine score and the unreturned pages with the highest score in each set are iteratively compared.

General observations about rough sets based hybrid systems are presented. Some challenges of existing hybrid systems and directions for future research are also indicated. 1 Introduction Data mining is a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases [1]. Soft computing [2], a consortium of methodologies in which fuzzy sets, neural networks, genetic algorithms and rough sets are principle members, has been widely applied to deal with various problems that contain uncertainty or imprecision in many fields, especially in data mining [3].

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