Outlier detection is an interesting issue in data mining and machine learning. In this paper, to detect outliers, an information-entropy-based k-nearest neighborhood relevant outlier factor algorithm is proposed that is combined with Shannon information theory and the triangle pruning strategy. The algorithm accounts for the data points whose k-nearest neighbors are distributed on the edge of the range within the designated radius. In particular, the neighborhood influence on each point is considered to address the problem of information concealment and submergence. Information entropy is used to calculate the weights to distinguish the importance of each attribute. Then, based on the attribute weights, the improved pruning strategy reduces the computational complexity of the subsequent procedures by removing some inliers and obtaining the outlier candidate dataset. Finally, according to the weighted distance between the objects in the candidate dataset and those in the original dataset, the algorithm calculates the dissimilarity between each object and its k-nearest neighbors. The data points with the top $r$ dissimilarity are regarded as the outliers. Experimental results show that, compared to existing methods, the proposed approach improves pruning and detection rates while maintaining the coverage rate.
YuW.F. and WangN., Research on credit card fraud detection model based on distance sum, in: Proceedings of IEEE International Joint Conference on Artificial Intelligence (JCAI'09) (2009), 353-356.
2.
GanjiV.R. and MannemS.N.P., Credit card fraud detection using anti-k nearest neighbor algorithm, International Journal on Computer Science and Engineering (IJCSE)4(6) (2012), 1035-1039.
3.
ZhangJ. and ZulkernineM., Anomaly based network intrusion detection with unsupervised outlier detection, in: Proceedings of IEEE International Conference on Communications (ICC'06) (2006), 2388-2393.
4.
GogoiP., BhattacharyyaD.K., BorahB. and KalitaJ.K., A survey of outlier detection methods in network anomaly identification, The Computer Journal54(4) (2011), 570-588.
5.
JunejoI.N. and ForooshH., Trajectory rectification and path modeling for video surveillance, in: Proceedings of the 11th International Conference on Computer Vision, ICCV 2007, IEEE, (Section 4), (2007), 1-7.
6.
LeeS. and HoY.S., Temporally consistent depth video filter using temporal outlier reduction, Signal, Image and Video Processing (6) (2014), 1-8.
7.
Martinez-CamaraM., VetterliM. and StohlA., Outlier removal for improved source estimation in atmospheric inverse problems, in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (2014), 6820-6824.
8.
PodgorelecV., HeričkoM. and RozmanI., Improving mining of medical data by outliers prediction, in: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) (2005), 91-96.
9.
HuangB. and YangP., Finding key knowledge attribute subspace of outliers in high-dimensional dataset, Expert Systems with Applications38(8) (2011), 10147-10152.
10.
AggarwalC.C., Applications of outlier analysis, Outlier Analysis, New York: Springer, (2013), 373-400.
11.
BarnettV. and LewisT., Outliers in statistical data, New York: John Wiley and Sons, 1994.
12.
KnorrE.M. and NgR.T., Algorithms for mining distance-based outliers in large datasets, in: Proceedings of the 24th International Conference on Very Large Data Bases (1998), 392-403.
13.
KnorrE.M. and NgR.T., Finding intensional knowledge of distance-based outliers, in: Proceedings of the 25th International Conference on Very Large Data Bases (1999), 211-222.
14.
KnorrE.M., NgR.T. and TucakovV., Distance-based outliers: Algorithms and applications, The VLDB Journal-The International Journal on Very Large Data Bases8(3-4) (2000), 237-253.
15.
HanJ., KamberM. and PeiJ., Data mining: Concepts and techniques, 3rd Edition, Morgan Kaufmann Publishers, Waltham, MA, USA, 2012.
16.
HeZ., XuX. and DengS., Discovering cluster based local outliers, Pattern Recognition Letters24(3) (2003), 1641-1650.
17.
BreunigM.M., KriegelH.P., NgR.T. and SanderJ., LOF: Identifying density-based local outliers, in: Proceedings of the ACM SIGMOD International Conference on Management of Data (2000), 93-104.
18.
KovacsL., VassD. and VidacsA., Improving quality of service parameter prediction with preliminary outlier detection and elimination, in: Proceedings of the Second International Workshop on Inter-domain Performance and Simulation (IPS 2004), Budapest, (2004), 194-199.
19.
JiangF. and ChenY.M., Outlier detection based on granular computing and rough set theory, Applied Intelligence42(2) (2015), 303-322.
20.
RamaswamyS., RastogiR. and ShimK., Efficient algorithms for mining outliers from large data sets, in: Proceedings of the ACM SIGMOD International Conference on Management of Data (2000), 427-438.
21.
AngiulliF. and PizzutiC., Fast outlier detection in high dimensional spaces, in: Proceedings of the 6th European Conference on the Principles of Data Mining and Knowledge Discovery (2002), 15-26.
22.
AngiulliF. and PizzutiC., Outlier mining in large high-dimensional data sets, IEEE Transactions on Knowledge and Data Engineering(TKDE)17(2) (2005), 203-215.
23.
AngiulliF. and FassettiF., DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets, ACM Transactions on Knowledge Discovery From Data (TKDD)3(1) (2009), 4: 1-57.
24.
ChenY., MiaoD. and ZhangH., Neighborhood outlier detection, Expert Systems with Applications37(12) (2010), 8745-8749.
25.
AngiulliF., BastaS. and PizzutiC., Distance-based detection and prediction of outliers, IEEE Transactions on Knowledge and Data Engineering (TKDE)18(2) (2006), 145-160.
26.
YangP. and ZhuQ., Finding key attribute subset in dataset for outlier detection, Knowledge-Based Systems24(2) (2011), 269-274.
27.
ShannonC.E., A mathematical theory of communication, Bell System Technical Journal27(3) (1948), 379-423.
28.
BayS.D. and SchwabacherM., Mining distance-based outliers in near linear time with randomization and a simple pruning rule, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '03, New York, USA, ACM Press, (2003), 29-38.
29.
HawkinsS., HeH., WilliamsG. and BaxterR., Outlier detection using replicator neural networks, in: Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery, Berlin Heidelberg, Springer, (2002), 170-180.
30.
AggarwalC.C. and YuP.S., Outlier detection for high dimensional data, Acm Sigmod Record30(5) (2002), 37-46.
31.
HeZ., XuX. and DengS., An optimization model for outlier detection in categorical data, Advances in Intelligent Computing, Berlin Heidelberg: Springer, (2005), 400-409.
32.
SuryaT. and AzhagusundariB., Spatial outlier detection approaches and methods: A survey, International Journal of Innovative Research and Development3(4) (2014), 24-29.
33.
CaiQ., HeH. and ManH., Spatial outlier detection based on iterative self-organizing learning model, Neurocomputing117(14) (2013), 161-172.
34.
AchtertE., HettabA., KriegelH.P., SchubertE. and ZimekA., Spatial outlier detection: Data, algorithms, visualizations, Advances in Spatial and Temporal Databases, Springer Berlin Heidelberg (2011), 512-516.
