Clustering is a process of classifying data into different classes or clusters, so objects in the same cluster have great similarities, but objects between different clusters have great differences. From a statistical point of view, cluster analysis is a method of simplifying data through data modeling. Traditional statistical clustering analysis methods include systematic clustering, decomposition, addition, dynamic clustering, ordered sample clustering, overlapping clustering and fuzzy clustering. Cluster analysis tools using k-means, k-center points and other algorithms have been added to many famous statistical analysis software packages, such as SPSS, SAS, etc. Cluster analysis is an exploratory analysis. In the classification process, people do not need to give a classification standard in advance. Cluster analysis can start from sample data and automatically classify. Different methods used in cluster analysis often lead to different conclusions. Different researchers perform cluster analysis on the same set of data, and the number of clusters obtained may not be the same.
MedAI has many years of experience in cluster analysis. We can provide you with professional cluster analysis services:
|Project name||Cluster analysis service|
|Cycle||Depends on the time you need to simulate and the time required for the system to reach equilibrium.|
|Product delivery mode||The simulation results provide you with the raw data and analysis results of molecular dynamics.|
MedAI provides corresponding professional cluster analysis service. Our cluster analysis service has proven to be very useful for understanding the biochemical basis of physiological events at different stages of drug development (even in different fields such as materials science). The MedAI team has worked in this field for more than a decade and published his findings in top scientific journals. If you need network analysis services, please feel free to contact us.
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