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UOT 004
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Biblioqrafik təsvir | Alguliyev , R.M. An Anomaly Detection Based on Optimization / R.M. Alguliyev , R.M. Aliguliyev , Y.N. Imamverdiyev , L.V. Sukhostat // I.J. Intelligent Systems and Applications. - 2017. - N: 12, vol.9.- P. 87-96. | Annotasiya | At present, an anomaly detection is one of the
important problems in many fields. The rapid growth of
data volumes requires the availability of a tool for data
processing and analysis of a wide variety of data types.
The methods for anomaly detection are designed to detect
object‘s deviations from normal behavior. However, it is
difficult to select one tool for all types of anomalies due
to the increasing computational complexity and the nature
of the data. In this paper, an improved optimization
approach for a previously known number of clusters,
where a weight is assigned to each data point, is proposed.
The aim of this article is to show that weighting of each
data point improves the clustering solution. The
experimental results on three datasets show that the
proposed algorithm detects anomalies more accurately. It
was compared to the k-means algorithm. The quality of
the clustering result was estimated using clustering
evaluation metrics. This research shows that the proposed
method works better than k-means on the Australia
(credit card applications) dataset according to the Purity,
Mirkin and F-measure metrics, and on the heart diseases
dataset according to F-measure and variation of
information metric. | Elektron variant | Elektron variant |
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