Automated Two Level Variable for Multiview Clustering
نویسندگان
چکیده
Clustering is used to identify the relationship among different objects from large volume of data. The clustering analysis is feasible only when the groups are formed with important features. The existing K-Means clustering processing time and the computation cost is high. The proposed two level variable weighting algorithm calculates weights for both views and variables to identify the important Properties. Due to automatic calculation, clustering can be done effectively in a smaller number of steps. The performance of Two-variable K-means is compared with five clustering algorithms to measure clustering accuracy in multiview data.
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