Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Chaotic Feature Selection and Reconstruction in Time Series Prediction Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Anomaly Detection Using Correctness Matching Through a Neighborhood Rough Set

Authors : Pey Yun Goh, Shing Chiang Tan, Wooi Ping Cheah

Published in: Neural Information Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Fast Agglomerative Information Bottleneck Based Trajectory Clustering
next chapter Learning Class-Informed Semantic Similarity
Literature
1.
Chen, D., Lu, C.-T., Kou, Y., Chen, F.: On detecting spatial outliers. GeoInformatica 12(4), 455–475 (2008)CrossRef
2.
Goldstein, M., Dengel, A.: Histogram-based outlier score (HBOS): a fast unsupervised anomaly detection algorithm. In: 35th German Conference on AI (KI-2012), pp. 59–63 (2012)
3.
Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Continuous monitoring of distance-based outliers over data streams. In: IEEE 27th International Conference on Data Engineering (ICDE), pp. 135–146. IEEE, Hannover (2011)
4.
Ye, M., Li, X., Orlowska, M.E.: Projected outlier detection in high-dimensional mixed-attributes data set. Expert Syst. Appl. 36(3), 7104–7113 (2009). Part 2CrossRef
5.
Suri, N.N.R.R., Murty, M.N., Athithan, G.: Detecting outliers in categorical data through rough clustering. Natural Comput. 15, 1–10 (2015)MathSciNet
6.
Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 178(18), 3577–3594 (2008)MathSciNetCrossRefMATH
7.
8.
Albanese, A., Pal, S.K., Petrosino, A.: Rough sets, kernel set, and spatiotemporal outlier detection. IEEE Trans. Knowl. Data Eng. 26(1), 194–207 (2014)CrossRef
9.
Li, X., Rao, F.: Outlier detection using the information entropy of neighborhood rough sets. J. Inf. Comput. Sci. 9(12), 3339–3350 (2012)
10.
Jiang, F., Sui, Y., Cao, C.: Some issues about outlier detection in rough set theory. Expert Syst. Appl. 36(3), 4680–4687 (2009). Part 1CrossRef
11.
12.
Aggarwal, C.C., Yu, P.S.: Outlier Detection for High Dimensional Data. SIGMOD Rec. 30(2), 37–46 (2001)CrossRef
13.
Hawkins, S., He, H., Williams, G.J., Baxter, R.A.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170–180. Springer, Heidelberg (2002)CrossRef
Metadata
Title
Anomaly Detection Using Correctness Matching Through a Neighborhood Rough Set
Authors
Pey Yun Goh
Shing Chiang Tan
Wooi Ping Cheah
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46674-3

Electronic ISBN: 978-3-319-46675-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46675-0_47

Premium Partner

    Image Credits