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Approach based on wavelet analysis for detecting and amending anomalies in dataset 被引量:1

Approach based on wavelet analysis for detecting and amending anomalies in dataset
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摘要 It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality. It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others' in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis' properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.
出处 《Journal of Central South University of Technology》 EI 2006年第5期491-495,共5页 中南工业大学学报(英文版)
基金 Project(50374079) supported by the National Natural Science Foundation of China
关键词 数据预处理 小波分析 异常检测 数据挖掘 data preprocessing wavelet analysis anomaly detecting data mining
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  • 1Bay S D,,Schwabacher M.Mining distance-based out- liers in near linear ti me with randomization and a si m- ple pruning rule[].Proceedings of the Ninth ACM SIGKDDInternational Conference on Knowledge Dis- covery and Data Mining.2003
  • 2Eskin E.Anomaly detection over noisy data using learned probability distributions[].Proceedings of the Seventeenth International Conference on Machine Learning ( ICML- ).2000
  • 3Knorr EM,Ng RT.Algorithms for mining distance-based outliers in large datasets[].Proceedings of the th VLDB Conference.1998
  • 4Knorr E M,Ng R T.Finding intentional knowledge of distance-based outliers[].Proceedings of the th International Conference on Very Large Data Bases.1999
  • 5Ramaswamy S,Rastogi R,Shim K.Efficient algorithms for mining outliers from large data sets[].Proceedings of the ACM SIGMOD international conference on Management of data.2000
  • 6Breunig M M,Kriegel H P,Ng R T,et al.OPTICS-OF:iden-tifying local outliers[].Procof the rd European Conference on Principles and Practice of Knowledge Discovery in Databases.1999
  • 7Breunig M,Kriegel HP,Ng RT,et al.LOF: Identifying Density-Based Local Outliers[].proceedings of ACM SIGMOD International Conference on Management of Data.2000
  • 8Jiang M F,Tseng S S,Su C M.Two-phase clustering process for outliers detection[].Pattern Recognition.2001
  • 9Larn R H,Yang J R.The effect of compressive deformation of austenite on the bainitic ferrite transformation in Fe-Mn-Si-C steels[].Journal of Materials Science.2000
  • 10Yamanishi K,Takeuchi J I,Williams G.On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms[].Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2000

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  • 1刘建国,李志舜,刘东.基于平稳小波变换及奇异值分解的湖底回波分类[J].声学学报,2006,31(2):167-172. 被引量:12
  • 2IVAN P,RANEE J M.Similarity search over time-series data using wavelets[].Proceedings of the th International Conference on Data Engineering.2002
  • 3GROUTAGE D,BENNINKK D.Feature sets for non-stationary signals derived from moments of singular value decomposition of Cohen-Posch distributions[].IEEE Transactions on Signal Processing.2006
  • 4HU Han-hui,YANG Hong,TAN Qing,YI Nian-en.Sintering fan faults diagnosis based on wavelet analysis[].Journal of Central South University: Science and Technology.2007
  • 5AGRAWALl R,FALOUTSOS C,SWAMI A.Efficient similarity search in sequence databases[].Proceeding of the th International Conference on Foundations of Data Organization and Algorithms.1993
  • 6RAFIEI D,MENDELZON A.Efficient retrieval of similar time sequences using DFT[].Proceedings of the th International Conference on Foundation of Data Organizations and Algorithms.1998
  • 7Chan Franky Kin-Pong,Fu Ada Wai-Chee,Yu Clement.Haar wavelets for efficient si milarity search of ti me-series:With and without ti me warping[].IEEE Transactions on Knowledge and Data Engineering.2003
  • 8Konstantinides,K.,Yao,K.Statistical analysis of effective singular values in matrix rank determination[].IEEE Transactions on Applied Superconductivity.1988
  • 9AKRITAS A G,MALASCHONOK G I.Applications of singular value decomposition(SVD)[].Mathematics and Computers in Simulation.2004
  • 10VRABIE V D,MARS J I,LACOUME J L.Modified sin-gular value decomposition by means of independent component analysis[].Signal Processing.2004

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