This huge amount of data is referred to as Big Data and the task of handling it comes in Big Data Analytics. The various data mining techniques proposed till date serve as an aid to the problem of efficiently analyzing, visualizing and storing Big Data. The K-means clustering algo...
"Big Data" can mean different things to different people. The scale and challenges of Big Data are often described using three attributes, namely volume, v... C Wu,R Buyya,K Ramamohanarao - 《Big Data》 被引量: 19发表: 2016年 Use of machine learning in big data analytics for insider...
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...
Big data is, therefore, defined with three attributes of volume, velocity, and variety that are called Gartner’s commentary; some scholars have in addition; IBM cited the fourth attribute and added ‘veracity’ for big data. Zikopoulos et al. [5] described that “V” or veracity dimension,...
Machine learning is dedicated to the study of how to use experience to improve the performance of the system itself by means of computation. Machine learning, as the main technique in big data analy…
Cluster analysis, or clustering, is a fundamental data mining task and diverse tool for big data analytics with vast applications. During a clustering process, the user is typically required toprovide input such as specification of parameters to guide the search for the target clustering. However,...
- 《Big Data》 被引量: 8发表: 2014年 EDDPC:一种高效的分布式密度中心聚类算法 与简单的 MapReduce 分布式实现比较,EDDPC 可以达到40倍左右的性能提升.%Clustering is a commonly used method for data relationship analytics in data mining ... 巩树凤,张岩峰 - 《计算机研究与发展》 被引量: 0发表: ...
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 ...
Contraction Clustering (RASTER) is a single-pass algorithm for density-based clustering of 2D data. It can process arbitrary amounts of data in linear time and in constant memory, quickly identifying approximate clusters. It also exhibits good scalabilit
Both the re-building and insertion of data are very efficient. We have chosen R [7], since it is open source, has a vibrant and active community, and is among the most widely used languages/platforms for data analytics. 1.1. A deeper look into DCEM EM-T (we use ‘T’ to denote ...