A popular unsupervised learning method, known as clustering, is extensively used in data
mining, machine learning and pattern recognition. The procedure involves grouping of …

A wide range of disparate variety of heterogeneous and even disparate data sources has
been integrated into the computer science research principles through the assistance of …

The technical advancements in big data have become popular and most desirable among
users for storing, processing, and handling huge data sets. However, clustering using these …

Continuous changes and the high calculation volume in network data distribution have
made it more difficult to detect abnormal behaviors within and analyze data. For this cause …

Clustering is one of the most important unsupervised machine learning tasks, which is
widely used in information retrieval, social network analysis, image processing, and other …

With the increasing number of telecom providers and services, churn prediction gains
tremendous interest in the current decade. The prediction models based on machine …

The occurrence of natural and man-made disasters usually leads to significant social and
economic disruption, as well as high numbers of casualties. Such occurrences are difficult to …

Data Mining is the discovery of “models” of data. Data dredging is a process of derogatory
referring to attempts for extracting information that was not supported by the data. Today …

The traditional K-means algorithm is very sensitive to the selection of clustering centers and
the calculation of distances, so the algorithm easily converges to a locally optimal solution …

为了能够及时了解Spark 环境下经典聚类算法K means 的最新研究进展, 把握K means
算法当前的研究热点和方向, 针对K means 算法的初始中心点优化研究进行综述 …