An Agent Oriented Approach for Implementation of the Range Method of Initial Centroids in K-Means Clustering Data Mining Algorithm

Intelligent agents are today accepted as powerful tools for data mining in a distributed environment. Artificial Intelligence (AI) algorithms are utilized to render software agents more intelligent. Knowledge discovery and data mining methods have been applied to discover hidden patterns and relations in complex datasets where clustering is one of the most important approaches used for this purpose. There exists a number of clustering algorithms among which the K-means algorithm is commonly used to find clusters due to its simplicity of implementation and fast execution. It appears extensively in the machine learning literature and in most data mining suite of tools. The algorithm is significantly sensitive to the selection of initial centroids. In this paper, we will present an agent oriented approach for implementation of the Range Method of initial centroids in K-means data mining algorithm. This Range Method is based on the actual sample datapoints. We have tested this method with both Euclidean and City Block (Manhattan) distances formulae on a different number of real life datasets.

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