The K-means clustering algorithm, though proposed more than 50-years ago, serves to be an excellent data mining solution able to cluster this increasing size of data. This paper discusses the various issues encountered in Big Data Analytics over the years and the relevance of the K-...
K-Means Clustering in Big Data Analytics - Explore K-Means Clustering, a powerful algorithm in Big Data Analytics. Learn how it works, its applications, and implementation techniques.
Machine learning, as the main technique in big data analytics applications, has been widely used in various fields. Machine learning algorithms include two main categories, one is supervised learning algorithms and the other is unsupervised learning algorithms. Cluster analysis is one of the unsupervise...
Big Data analytics are recently coming up as prominent research area in the field of data science. Apache Spark is an open source distributed data processing platform that uses distributed memory...doi:10.1007/978-3-319-74690-6_41Omar Hesham Mohamed...
Palanisamy V, Thirunavukarasu R (2019) Implications of big data analytics in developing healthcare frameworks–a review. J King Saud Univ-Comput Inf Sci 31:415–425 Google Scholar Pandit S, Gupta S (2011) A comparative study on distance measuring approaches for clustering. Int J Res Comput ...
Big data analytics, which studies large amounts of data of various types to disclose hidden patterns, is attracting more and more attention in both academic and industrial areas. One of the most significant problem of big data analytics is the task of clustering analysis, which is to group sim...
As volume and veracity become more prominent, big data analytics is considered as the most popular data analytics topic that will really influence how the world performs. In this project, PySpark language is employed to solve the big data analytics for smart meters. ...
Commonly utilized techniques include data compression, machine learning, correlation analysis and clustering for data processing and analytics in IoT [[3], [5]]. As one of the most leading big data mining approaches for drilling smart data, clustering attempts to divide the raw objects into ...
In the recent literature, axiomatic frameworks have been proposed for clustering and its quality. But none of the proposed frameworks has concentrated on the computational aspects of clustering, which is essential in current big data analytics. In this paper, we propose an axiomatic framework for ...
In this dendrogram, data points A, B, and C are given at the bottom, while the branches above them will represent the clusters that each belongs to. At every level, the distance or similarity of the clusters can be shown on the vertical axis. In this example, A and B happen to be ...