Optimization of Artificial Bee Colony Algorithm for Clustering in Data Mining

Data mining is a powerful new technology, which aims at the extraction of hidden predictive information from large databases. Data pre-processing involves many tasks including detecting outliers, recovering incomplete data and correcting errors. Outlier detection is an important pre-processing task. It is a task that finds objects that are dissimilar or inconsistent with respect to the remaining data. It has many uses in applications like fraud detection, network intrusion detection and clinical diagnosis of diseases. Using clustering algorithms for outlier detection is a technique that is frequently used. The clustering algorithms consider outlier detection only to the point they do not interfere with the clustering process. In this paper, an efficient method has been proposed which is based on Fuzzy clustering using Artificial Bee Colony algorithm for detecting the outliers.

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