A novel approach for outlier detection with iterative clustering( ICOD) in diverse subspaces is proposed. The proposed methodology comprises two phases,iterative clustering and outlier factor computation. During the c...A novel approach for outlier detection with iterative clustering( ICOD) in diverse subspaces is proposed. The proposed methodology comprises two phases,iterative clustering and outlier factor computation. During the clustering phase, multiple clusterings are detected alternatively based on an optimization procedure that incorporates terms for cluster quality and novelty relative to existing solution. Once new clusters are detected,outlier factors can be estimated from a new definition for outliers( cluster based outlier), which provides importance to the local data behavior. Experiment shows that the proposed algorithm can detect outliers which exist in different clusterings effectively even in high dimensional data sets.展开更多
Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outl...Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.展开更多
基金Natural Science Foundation Project of CQ CSTC(Nos.cstc2012jjA 40002,cstc2012jjA 40016)Fundamental Research Funds for the Central Universities,China(No.0216005207016)
文摘A novel approach for outlier detection with iterative clustering( ICOD) in diverse subspaces is proposed. The proposed methodology comprises two phases,iterative clustering and outlier factor computation. During the clustering phase, multiple clusterings are detected alternatively based on an optimization procedure that incorporates terms for cluster quality and novelty relative to existing solution. Once new clusters are detected,outlier factors can be estimated from a new definition for outliers( cluster based outlier), which provides importance to the local data behavior. Experiment shows that the proposed algorithm can detect outliers which exist in different clusterings effectively even in high dimensional data sets.
基金Project(61362021)supported by the National Natural Science Foundation of ChinaProject(2016GXNSFAA380149)supported by Natural Science Foundation of Guangxi Province,China+1 种基金Projects(2016YJCXB02,2017YJCX34)supported by Innovation Project of GUET Graduate Education,ChinaProject(2011KF11)supported by the Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education,China
文摘Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.
