An Outlier Detection Method Based On Artificial Bee Colony Fuzzy Clustering

Poonam Chalotra, Maitreyee Dutta


There is a need for pre-processing of the raw data in many fields, such as data mining, information retrieval, machine learning and
pattern recognition. Data Mining or Knowledge discovery refers to a variety of techniques that have developed in the fields of databases,
machine learning and pattern recognition. Data pre-processing involves many tasks including detecting outliers, recovering incomplete data and
correcting errors. Outlier detection is an important pre-processing task. Outlier detection is a task that finds objects that are dissimilar or
inconsistent with respect to the remaining data. Outlier detection can be done using clustering methods. In this paper, an efficient outlier
detection method has been proposed which is based on Fuzzy clustering using Artificial Bee Colony algorithm. The Fuzzy clustering based on
Artificial Bee Colony algorithm is performed, and small clusters are calculated and considered as outlier clusters. Fuzzy clustering is used to
choose the cluster heads and ABC to select the members of the clusters. Test result shows the effective results in finding the outliers on data sets
in data mining literature.

Keywords— Artificial Bee Colony algorithm, Clustering, Data mining, Fuzzy C-Means clustering, Outliers.

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