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S rensen-Dice Similarity Indexing based Weighted
Big data is a collection of large volume of data and extract similar data points from large dataset. Clustering is an
essential data mining technique for examining large volume of data. Several techniques have been developed for handling big
dataset. However, with much time consumption and space complexity, accuracy is said to be compromised. In order to
improve clustering accuracy with less complexity, Sørensen-Dice Indexing based Weighted Iterative X-means Clustering (SDI-
WIXC) technique is introduced. SDI-WIXC technique is used for grouping the similar data points with higher clustering
accuracy and minimal time. First, number of data points is collected from big dataset. Then, along with the weight value, the
given dataset is partitioned into ‘X’ number of clusters. Next, based on the similarity measure, Weighted Iterated X-means
Clustering (WIXC) is applied for clustering data points. Sørensen-Dice Indexing Process is used for measuring similarity
between cluster weight value and data points. Upon similarity found between weight value of cluster and data point, data
points are grouped into a specific cluster. Besides, the WIXC method also improves the cluster assignments through repeated
subdivision using Bayesian probability criterion. This in turn helps to group all data points and hence, improving the
clustering accuracy. Experimental evaluation is carried out with number of factors such as clustering accuracy, clustering
time and space complexity with respect to the number of data points. The experimental results reported that the proposed SDI-
WIXC technique obtains high clustering accuracy with minimum time as well as space complexity.
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