Cluster Computing

, Volume 22, Supplement 2, pp 3613–3619 | Cite as

Research on abnormal data mining algorithm based on ICA

  • Jiangke Cheng
  • Xiaodong MaiEmail author
  • Shengnan Wang


The research of abnormal data mining is very important for ensuring the reliable operation of data mining. The algorithm currently used in data mining of anomaly mining is only a kind of measuring error time or spatial correlation. Considering the shortcomings of the existing outlier data mining algorithms and the temporal and spatial correlation of the error matrix, an abnormal data mining algorithm based on ICA is proposed. In this algorithm, the ability of reducing the dimension of BSA and the multi-scale modeling ability of wavelet transform are used to construct the algorithm of abnormal data mining. In the analysis of residual anomaly data, it is realized mainly by EWMA and Shewart control chart. And the sliding window mechanism is applied to realize the online expansion of outlier data mining algorithm, and online ICA outlier data mining algorithm is obtained. By analyzing the outlier data and the simulation results, we can conclude that compared with the BSA algorithm and the KLE algorithm, the ICA algorithm has more outstanding advantages and better detection performance.


Abnormal data mining research ICA Detection error Online expansion Wavelet transform 


  1. 1.
    Berka, P.: Knowledge discovery in databases and data mining. Am. Sci. 7(4), 197–198 (2015)Google Scholar
  2. 2.
    Santos, I., Brezo, F., Ugarte-Pedrero, X., Bringas, P.G.: Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf. Sci. 231(9), 64–82 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Lausch, A., Schmidt, A., Tischendorf, L.: Data mining and linked open data—new perspectives for data analysis in environmental research. Ecol. Model. 295, 5–17 (2015)CrossRefGoogle Scholar
  4. 4.
    Schneider, A.: Monitoring land cover change in urban and peri-urban areas using dense time stacks of landsat satellite data and a data mining approach. Remote Sens. Environ. 124, 689–704 (2012)CrossRefGoogle Scholar
  5. 5.
    Karimi, S., Wang, C., Metke-Jimenez, A., Gaire, R., Paris, C.: Text and data mining techniques in adverse drug reaction detection. ACM Comput. Surv. 47(4), 1–39 (2015)CrossRefGoogle Scholar
  6. 6.
    Vieira, M.A., Formaggio, A.R., Rennó, C.D., Atzberger, C., Aguiar, D.A., Mello, M.P.: Object based image analysis and data mining applied to a remotely sensed landsat time-series to map sugarcane over large areas. Remote Sens. Environ. 123(8), 553–562 (2012)CrossRefGoogle Scholar
  7. 7.
    Astolfi, D., Castellani, F., Garinei, A., Terzi, L.: Data mining techniques for performance analysis of onshore wind farms. Appl. Energy 148, 220–233 (2015)CrossRefGoogle Scholar
  8. 8.
    Mooney, B.L., Corrales, L.R., Clark, A.E.: Molecularnetworks: an integrated graph theoretic and data mining tool to explore solvent organization in molecular simulation. J. Comput. Chem. 33(8), 853–860 (2012)CrossRefGoogle Scholar
  9. 9.
    Ferreira, J.C., Almeida, J.D., Silva, A.R.D.: The impact of driving styles on fuel consumption: a data-warehouse-and-data-mining-based discovery process. IEEE Trans. Intell. Transp. Syst. 16(5), 2653–2662 (2015)CrossRefGoogle Scholar
  10. 10.
    Pruim, R.H., Mennes, M., Van, R.D., Llera, A., Buitelaar, J.K., Beckmann, C.F.: Ica-aroma: a robust ica-based strategy for removing motion artifacts from fmri data. Neuroimage 112, 267–277 (2015)CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematics and Computer SciencePanzhihua UniversityPanzhihuaChina
  2. 2.College of Information TechnologyGuangdong Industry PolytechnicGuangzhouChina
  3. 3.School of Biological and Chemical EngineeringPanzhihua UniversityPanzhihuaChina

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