Extended Jaccard Indexive Buffalo Optimized Clustering on Geo-social Networks with Big Data

Clustering is a general task of data mining where partitioning a large dataset into dissimilar groups is done. The enormous growth of Geo-Social Networks (GeoSNs) includes users, who create millions of heterogeneous data with a variety of information. Analyzing such volume of data is a challenging task. The clustering of large volume of data is used to identify the frequently visited location information of the users in Geo-Social Networks. In order to improve the clustering of a large volume of data, a novel technique called Extended Jaccard Indexive Buffalo Optimized Data Clustering (EJIBODC) is introduced for grouping the data with high accuracy and less time consumption. The main aim of EJIBODC technique is to partition the big dataset into different groups. In this technique, many clusters with centroids are initialized to group the data. After that, Extended Jaccard Indexive Buffalo Optimization technique is applied to find the fittest cluster for grouping the data. The Extended Jaccard Index is applied in the Buffalo Optimization to measure the fitness between the data and the centroid. Based on the similarity value, using a gradient ascent function, the data finds the fittest cluster centroid for grouping. After that, the fitness value of cluster is updated and all the data gets grouped into a suitable cluster with high accuracy and minimum error rate. An experimental procedure is involved with big geo-social dataset and testing of different clustering algorithms. The series discussion is carried out on factors such as clustering accuracy, error rate, clustering time and space complexity with respect to a number of data. Experimental outcomes demonstrate that the proposed EJIBODC technique achieves improved performance in terms of higher clustering accuracy, less error rate, time consumption and space complexity when compared to previous related clustering techniques.