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This paper presents a data clustering approach using modified K-Means algorithm based on the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm partitions the whole space into different segments and calculates the frequency of data point in each segment. The segment which shows maximum frequency of data point will have the maximum probability to contain the centroid...
Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields. When there is a need to learn the inherent grouping structure of data in an unsupervised manner, ant-based clustering stand out as the most widely used group of swarm-based clustering algorithms. Under this perspective, this paper presents a new...
In this paper an efficient method for data clustering is proposed. The proposed algorithm is a modified psFCM, called the pshFCM clustering algorithm that finds better cluster centers for a given data sets as compared to the cluster centers obtained by he sFC. Tecmpuatinalperormnceof he ropsed pshFCM algorithm is comparable with thepsFCM and the FCM.
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