This paper reports a Bayesian approach for the automatic identification of the optimal clustering proposal in the analysis of single-molecule localization-based super-resolution data.

Transitivity Clustering provides the end user with simple interfaces that facilitate each step of the data clustering workflow. Figure 1 illustrates those parts covered by this paper.

Recently, Ningbo Lanyuan Industrial City Group Co., Ltd. announced that it has obtained a patent titled "Intelligent Industrial Cluster Management System Based on Big Data Analysis ", with the ...

Course TopicsCluster analysis is a number of different algorithms and techniques for grouping objects sharing similar characteristics. Researchers in many areas face problems such as how to organize ...

Cluster analysis uses numerical methods to divide a group of units into homogeneous subgroups. Various methods of Cluster Analysis - Single Linkage, Average Linkage, Double Linkage and the k-means ...

Cluster analysis divides data into subsections that are meaningful and useful. This is an important tool in the social sciences, biology, statistics, pattern recognition and, now, marketing.

Multivariate analysis techniques may be used for several purposes, such as dimension reduction, clustering, or classification. The primary goal of this short course is to help researchers who want to ...

In the new round of smart industrial transformation, Ningbo Lanyuan Industrial City Group Co., Ltd. has recently delivered new achievements in the field of big data applications. The company has ...

Maria Carmela Lo Bue, Stephan Klasen, Identifying Synergies and Complementarities Between MDGs: Results from Cluster Analysis, Social Indicators Research, Vol. 113, No. 2, POVERTY, VULNERABILITY AND ...

The study, “Using Artificial Intelligence to Determine the Impact of E-Commerce on the Digital Economy,” builds a fused indicator matrix spanning ICT infrastructure, payments, trade and logistics, ...