Affiliations: [a] School of Territorial Resources and Tourism, Anhui Normal University, Wuhu, Anhui, China | [b] School of Mathematics and Computer Science, Anhui Normal University, Wuhu, Anhui, China
Correspondence:
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Corresponding author: Yonglong Luo, School of Territorial Resources and Tourism, Anhui Normal University, No. 189, south road of Jiuhua Road, Wuhu 241003, Anhui, China. Tel.: +86 0553 5910645; E-mail:[email protected]
Abstract: 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.
