DocumentCode :
3515763
Title :
Anomaly detection in data mining. Hybrid approach between filtering-and-refinement and DBSCAN
Author :
Handra, S.I. ; Ciocârlie, H.
Author_Institution :
Comput. & Software Eng. Dept., Politeh. Univ., Timişoara, Romania
fYear :
2011
fDate :
19-21 May 2011
Firstpage :
75
Lastpage :
83
Abstract :
Anomaly detection is a domain that represents the key for the future of data mining. We will try to present some key anomaly detection methods applicable in the data mining process. Some methods are existing techniques as the DBSCAN algorithm and some have just been presented to the public recently and could be the answer to future anomaly detection development. One example is the filtering-and-refinement approach, a new general two stage technique for more efficient and effective anomaly detection. This paper will try to illustrate the strengths and weaknesses of the classical techniques presented but as we will see the results are completely dependent on the data sets that are analyzed. We will emphasize on efficiency, robustness and accuracy. We will also try to demonstrate a hybrid approach obtained by combining the filtering-and-refinement method with the DBSCAN algorithm. In our experiments we pursued to compare the performance of the normal DBSCAN algorithm with the performance of the hybrid one. Our results indicate that the hybrid method is more accurate in terms of detecting anomalies and far superior in terms of speed than the normal DBSCAN algorithm.
Keywords :
data mining; security of data; DBSCAN algorithm; anomaly detection methods; data mining process; data sets; filtering-and-refinement method; Accuracy; Adaptation model; Algorithm design and analysis; Clustering algorithms; Data mining; Intrusion detection; Radio frequency; Anomaly detection; clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Computational Intelligence and Informatics (SACI), 2011 6th IEEE International Symposium on
Conference_Location :
Timisoara
Print_ISBN :
978-1-4244-9108-7
Type :
conf
DOI :
10.1109/SACI.2011.5872976
Filename :
5872976
Link To Document :
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