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.
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...
Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term lo
Discover the latest articles and news from researchers in related subjects, suggested using machine learning. Big Data Data Analytics Data Mining Data processing Functional clustering Time Series Analysis References Akaike, H. (1981). Likelihood of a model and information criteria. Journal of ...
In addition to the metaheuristic algorithm, the primary goal of the data mining process is to gather data from a big data set. The data can then be translated into a clear format for further usage. Clustering is a popular experimental data analysis tool. Objects are arranged using clustering ...
Literature review on data analytics for social microblogging platforms 2.4 Cluster analysis of microblogs Cluster analysis is an important data mining technique [100,101] that is widely used in many fields. Clustering is the method to identify groups of homogeneous objects (which are placed in the ...
Exploring performance and predictive analytics of agriculture data 17.2Technique used in data mining 17.2.1Clustering Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same...
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 ...
Big data has become popular for processing, storing and managing massive volumes of data. The clustering of datasets has become a challenging issue in the field of big data analytics. The K-means algorithm is best suited for finding similarities between entities based on distance measures with sma...