Large Scale Data Using K-Means

Abstract

Regular data base questioning tactics are insufficient to extract meaningful data due to the exponential expansion of high layered datasets; therefore, analysts nowadays are forced to build new processes to satisfy the increased needs. Because of the development in the number of data protests as well as the expansion in the number of elements/ascribes, such vast articulation data leads to numerous new computational triggers. To increase the effectiveness and accuracy of mining activities on highly layered data, the data should be preprocessed using a successful dimensionality decrease technique. So we have collected ideas of different researchers. In several fields, cluster analysis has recently gained popularity as a method for data analysis. A popular parceling-based clustering method called K-means searches for a certain number of clusters that may be found by their centroids. However, the results are quite dependent on the original cluster focus sites. Once more, the number of distance calculations significantly grows as the complexity of the data increases. This is because building a high-precision model frequently necessitates a sizable and dispersed preparatory set. A large preparation set could also need a significant amount of preparation time. There is a trade-off between speed and accuracy when creating orders, especially for large data sets. Vector data are frequently clustered, packed, and summed using the k-means approach. We provide No Concurrent Specific Clumped K-means, a rapid and memory-effective GPU-based approach for cautious k-means (ASB K-means). In contrast to previous GPU-based k-means methods, which require stacking the entire dataset onto the GPU for clustering, our methodology may be tailored to consume far less GPU RAM than the size of the complete dataset. As a result, we may cluster datasets that are bigger than the available RAM. In order to effectively handle large datasets, the method employs a clustered architecture and applies the triangle disparity in each k-means focus to eliminate a data point on the off chance that its enrollment task, or the cluster it is a member of, remains unchanged. As a result, fewer data guides have to be sent between the Slam of the computer processor and the global memory of the GPU.

Analysis of H-index and Papers Citation in Computer Science Field using K-Means Clustering Algorithm

“…Furthermore, we have used Microsoft Power BI to make the analysis of this study. Power BI is a business analytics service provided by Microsoft that provides data visualization and interactive functionality as an easy way to visualize and analyze various types of data [11].…”

Integration of PSO-based advanced supervised learning techniques for classification data mining to predict heart failure

“…Despite various efforts, no single classifier has proven to be the best for all datasets. In this research, we present a novel approach that incorporates advanced supervised learning (ASL) [29]- [31] and particle swarm optimization (PSO) [32], [33] techniques to optimize classification results. Moreover, we employed split and cross-validation techniques with varying composition ratios of 70:30, 80:20, and 90:10, using k-fold=10, and tested twelve classifiers sorted into five groups: decision tree models (DTM), SVM, Naïve Bayes classifier models (NBCM), logistic regression models (LRM), and lazy models (LM).…”

“…Furthermore, our findings can guide future research endeavors aimed at enhancing the diagnosis and treatment of heart failure. The integration of AI and ML techniques [31], [32] in healthcare holds great promise for enhancing patient well-being and reducing healthcare expenses.…”