PENG Xiao-Qi, SONG Yan-Po, TANG Ying. Anomaly Processing Based on Wavelet Analysis[J]. INFORMATION AND CONTROL, 2005, 34(6): 676-679.
Citation: PENG Xiao-Qi, SONG Yan-Po, TANG Ying. Anomaly Processing Based on Wavelet Analysis[J]. INFORMATION AND CONTROL, 2005, 34(6): 676-679.

Anomaly Processing Based on Wavelet Analysis

More Information
  • Received Date: May 29, 2005
  • Published Date: December 19, 2005
  • It is difficult to detect the anomalies of which the relationship among its attributes is very different from one another in a data set.Aiming at this problem,an approach based on wavelet analysis to detect and amend the anomalous samples is proposed in this paper.Taking full advantage of wavelet analysis characteristics of multiple scale and local analysis,this approach is able to detect and amend anomalous samples accurately.To realize the rapid numerical computation of wavelet translation for a discrete sequence,a modified algorithm based on Newton-Cores formula is also proposed.The test results show that the approach is feasible,with good effect and practicality.
  • [1]
    Mehmed K.闪四清,等.数据挖掘——概念、模型、方法和算法[M].北京:清华大学出版社,2003.
    [2]
    Eleazar E.Anomaly detection over noisy data using learned probability distributions [A].Proceedings of the 17th International Conference on Machine Learning [DB/OL].http://www1.cs.columbia.edu/ids/publications/anomaly-icml00.ps,2000.
    [3]
    Knorr E K,Ng R T.Algorithms for mining distance-based outiers in large datasets [A].Proceedings of the 24th International Conference on Very Large Data Bases [C].USA:Morgan Kaufmann,1998.392~403.
    [4]
    Knorr E K,Ng R T.Finding intentional knowledge of distancebased outliers [A].Proceedings of the 25th International Conference on Very Large Data Bases [C].USA:Morgan Kaufmann,1999.211~222.
    [5]
    Breunig M M,Kriegsl H P,NgRT,etal.LOF:identifying density-based local outliers [A].Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data [C].New York,NY,USA:ACM Press,2000.93~104.
    [6]
    Breunig M M,Kriegsl H P,NgRT,etal.OPTICS-OF:identifying local outliers [A].Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases.Lecture Notes in Computer Science(LNAI 1704)[C].Prague:Springer,1999.262~270
    [7]
    Jiang M F,Tseng S S,Su C M.Two-phase clustering process for outliers detection [J].Pattern Recognition Letters,2001,22(6-7):691~700.
    [8]
    刘洪霖.化工冶金过程人工智能优化[M].北京:冶金工业出版社,1999.17~20.
    [9]
    Hawkins S,He H X,Williams G,et al.Outlier detection using replication neural networks [A].Proceedings of the 4th International Conference and Data Warehousing and Knowledge Discovery[C].London,UK:Springer-Verlag,2002:170~180.
    [10]
    杨福生.小波变换的工程分析与应用[M].北京:科学出版社,2000.
    [11]
    李庆扬,王超能,易大义.数值分析(第3版)[M].武汉:华中理工大学出版社,1986.122~128.
  • Related Articles

    [1]YANG Yang, DING Jiaman, LI Haibin, JIA Lianyin, YOU Jinguo, JIANG Ying. A Spark-based Frequent Patterns Mining Algorithm for Uncertain Datasets[J]. INFORMATION AND CONTROL, 2019, 48(3): 257-264. DOI: 10.13976/j.cnki.xk.2019.8371
    [2]LI Junjie, YANG Bo, LI Hongguang. Alarm Correlation Analysis Approach Based on Alarm Time Information Mining[J]. INFORMATION AND CONTROL, 2019, 48(1): 29-34. DOI: 10.13976/j.cnki.xk.2019.7494
    [3]HU Jiaojiao, WANG Xiaofeng, ZHANG Meng, ZHANG Depeng, HU Shaolin. Time-series Data Anomaly Detection Method Based on Deep Learning[J]. INFORMATION AND CONTROL, 2019, 48(1): 1-8. DOI: 10.13976/j.cnki.xk.2019.8062
    [4]ZHU Huiyun, CHEN Senfa, CAO Jie, ZHANG Lijie. Change Mining Method of Customer Classification Based on the Best Splitting Point[J]. INFORMATION AND CONTROL, 2012, 41(6): 668-674. DOI: 10.3724/SP.J.1219.2012.00668
    [5]REN Jia, SU Hong-ye, CHU Jian. Application of SOM-based Data Mining Methods to PX Absorption and Separation Process[J]. INFORMATION AND CONTROL, 2006, 35(1): 84-89,92.
    [6]XIA Ke-wen, SONG Jian-ping, LI Chang-biao. AN APPROACH TO OIL LOG DATA MINING BASED ON ROUGH SET & NEURAL NETWORK[J]. INFORMATION AND CONTROL, 2003, 32(4): 300-303.
    [7]LUO Yin-sheng, LI Rin-hou, MET Shi-chun. DATA MINING MODEL RESEARCH ON COMPLEX INDUSTRY PROCESS[J]. INFORMATION AND CONTROL, 2003, 32(1): 32-35.
    [8]YUAN Hong-chun, XIONG Fan-lun, HUAI Xiao-yong. SPATIAL DATA MINING AND ITS INTEGRATION FRAMEWORK WITH INTELLIGENT SYSTEM[J]. INFORMATION AND CONTROL, 2002, 31(4): 304-309.
    [9]ZHENG Bin-xiang, DU Xiu-hua, XI Yu-geng. A NEW ALGORITHM OF SIMILARITY MINING IN TIME SERIES DATA[J]. INFORMATION AND CONTROL, 2002, 31(3): 264-267,271.
    [10]QIN Zhenxing, YUAN Zengre. A PART OF OUR RESEARCH WORK AND VIEWS IN THE KDD AND DATA MINING[J]. INFORMATION AND CONTROL, 1999, 28(4): 255-261.

Catalog

    Article views (1742) PDF downloads (139) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return