ODABK: AN EFFECTIVE APPROACH TO DETECTING OUTLIER IN DATA STREAM
Currently, data mining in data stream becomes a very popular research field. One of the central tasks in mining data streams is that of identifying outliers which can lead to discovering unexpected and interesting knowledge, which is critical important. To effectively mine outliers in data stream,ODABK, an algorithm for outlier detection in data stream is presented. It is based on KNN and significantly enhanced by means of other data structures and its optimized logical operations. Finally, the paper reports experiments on a real-world census data which show that ODABK is more effective in detection rate and execution times.
Outlier detection data stream KNN-based neighborhood
FENG HAN YAN-MING WANG HUA-PENG WANG
School of Computer Science and Technology, North China Electric Power University, Baoding 071003, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1036-1041
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）