Rare Category Detection Algorithm Based on Cluster Separability
YAN Xuan-Hui, GUO Gong-De
School of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350007 Key Laboratory of Network Security and Cryptology of Fujian Province, Fujian Normal University, Fuzhou 350007
Abstract The rare category mining, which is an important research field in data mining, is widely applied. Aiming at the defects of the traditional rare category recognition methods, an rare category detection algorithm based on cluster separability(RDACS), is proposed based on the combination of density difference and inter-cluster separability criterion for rare category mining. An active-learning scenario is used to detect rare category. The similarity of feature weight is applied to the separability of rare category cluster and its surrounding samples. The experimental results on UCI public datasets and KDD99 datasets show that compared with the existing similar algorithms, the RDACS algorithm has an advantage in the number of inquiries, which can significantly improve the efficiency and reduce human errors. RDACS is complementary to the existing rare category recognition methods.
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2014, Vol. 27 
