Most datasets contain objects whose attributes differ significantly as compared to those of other objects in the same dataset. Although initially disregarded as noise such objects are now defined as outliers and detecting them can be beneficial for applications such as detecting fraudulent financial transactions, or intruders. A major challenge to detect these outliers is the dimensionality and vastness of data. Rough sets can be used to clearly define the objects that need not be considered as outliers. As a result, all objects do not need to be processed while applying the outlier detection algorithm. This paper exploits a new methodology for detecting outliers using rough sets. This methodology has high potential since outliers have a low probability of being in the boundary region defined by the intersection of the lower and upper approximation, as compared to the lower approximation. This use of rough sets can be used to significantly reduce the computation time of existing algorithms.
CadezISmythP. Probabilistic clustering using hierarchical models.
2.
EsterMKriegelHPSanderJ, et al.A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD. San Francisco, CA: Morgan Kaufmann Publishers Inc, 1996, pp.226–231.
3.
HawkinsDM. Identification of outliers. London: Chapman and Hall, 1980.
4.
HeZXuXDengS. Discovering cluster-based local outliers. Pattern Recognit Lett2003; 24: 1641–1650.
5.
KantardzicM. Data mining: concepts, models, methods, and algorithms. Hoboken, New Jersey: John Wiley & Sons, 2011.
6.
DuanLXuLLiuY,et al.Cluster-based outlier detection. Ann Oper Res2009; 168: 151–168.
7.
DongGXieM. Color clustering and learning for image segmentation based on neural networks. IEEE Trans Neural Netw2005; 16: 925–936.
8.
ZhangYCaoTLiS, et al.Parallel processing systems for big data: a survey. Proc IEEE2016; 104: 2114–2136.
9.
AlmeidaJABarbosaLMPaisAA,et al.Improving hierarchical cluster analysis: a new method with outlier detection and automatic clustering. Chemom Intell Lab Syst2007; 87: 208–217.
10.
GanjiVRMannemSN. Credit card fraud detection using anti-k nearest neighbor algorithm. Int J Comput Sci Eng2012; 4: 1035–1039.
11.
LührSLazarescuM. Incremental clustering of dynamic data streams using connectivity based representative points. Data Knowl Eng2009; 68: 1–27.
12.
ParkNHLeeWS. Statistical grid-based clustering over data streams. ACM SIGMOD Rec2004; 33: 32–37.
13.
ShaoJTannerSWThompsonN,et al.Clustering molecular dynamics trajectories: 1. Characterizing the performance of different clustering algorithms. J Chem Theory Comput2007; 3: 2312–2334.
14.
YuanZChenBLiuJ, et al.Anomaly detection based on weighted fuzzy-rough density. Appl Soft Comput2023; 134: 109995.
15.
MazarbhuiyaFAShenifyM. An intuitionistic fuzzy-rough set-based classification for anomaly detection. Appl Sci2023; 13: 1–15.
16.
KnoxEMNgRT. Algorithms for mining distance-based outliers in large datasets. In: Proceedings of the international conference on very large data bases, 1998, pp.392–403.
17.
PawlakZ. Rough sets. Int J Comput Inf Sci1982; 11: 341–356.
18.
BreunigMMKriegelHPNgRT, et al.LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, pp.93–104.
AlbaneseAPalSKPetrosinoA. Rough sets, kernel set, and spatiotemporal outlier detection. IEEE Trans Knowl Data Eng2012; 26: 194–207.
21.
KellerFMullerEBohmK. HiCS: high contrast subspaces for density-based outlier ranking. In: 2012 IEEE 28th international conference on data engineering, 2012, pp.1037–1048. IEEE.
22.
JiangFSuiYCaoC. Outlier detection using rough set theory. In: International workshop on rough sets, fuzzy sets, data mining, and granular-soft computing, 2005, pp.79–87. Berlin, Heidelberg: Springer Berlin Heidelberg.
23.
JiangFSuiYCaoC. Some issues about outlier detection in rough set theory. Expert Syst Appl2009; 36: 4680–4687.
24.
SinghMPamulaR. An outlier detection approach in large-scale data stream using rough set. Neural Comput Appl2020; 32: 9113–9127.
25.
JiangFSuiYCaoC. An information entropy-based approach to outlier detection in rough sets. Expert Syst Appl2010; 37: 6338–6344.
26.
ZerkoukMMihoubiMChikhaouiB. Deep generative model with isolation forest (DGM-IF) for unsupervised anomaly detection in wireless sensor network and internet of things. In: 2023 9th international conference on control, decision and information technologies (CoDIT), 2023, pp.2275–2280. IEEE.
27.
Maciá-PérezFBerna-MartinezJVOlivaAF,et al.Algorithm for the detection of outliers based on the theory of rough sets. Decis Support Syst2015; 75: 63–75.
28.
MazarbhuiyaFA. Detecting anomaly using neighborhood rough set based classification approach, https://ssrn.com/abstract=4124453 (2022 May 31).
29.
JiangFSuiYCaoC. A rough set approach to outlier detection. Int J Gen Syst2008; 37: 519–536.
30.
ShaariFBakarAAHamdanAR. Outlier detection based on rough sets theory. Intell Data Anal2009; 13: 191–206.
31.
JiangFChenYM. Outlier detection based on granular computing and rough set theory. Appl Intell2015; 42: 303–322.
32.
HanJKamberMPeiJ. Data mining concepts and techniques. 3rd ed. Amsterdam: Elsevier Science, 2012.
33.
HagenLKahngAB. New spectral methods for ratio cut partitioning and clustering. IEEE Trans Comput-Aided Des Integr Circuits Syst1992; 11: 1074–1085.
34.
ZerkoukMMihoubiMChikhaouiB. Deep generative model with isolation forest (DGM-IF) for unsupervised anomaly detection in wireless sensor network and internet of things. In: 2023 9th international conference on control, decision and information technologies (CoDIT), 2023, pp.2275–2280. Rome, Italy: IEEE. DOI: 10.1109/CoDIT58514.2023.10284184.
