Cluster Analysis Service

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.

Overall solutions

CD ComputaBio has many years of experience in cluster analysis. We can provide you with professional cluster analysis services:

• Connectivity-based clustering
  Connectivity-based clustering, also known as hierarchical clustering, is based on the core idea of objects being more related to nearby objects than to objects farther away. These algorithms connect "objects" to form "clusters" based on their distance. A cluster can be described largely by the maximum distance needed to connect parts of the cluster.
• Centroid-based clustering
  In centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set.
• Distribution-based clustering
  Distribution-based clustering produces complex models for clusters that can capture correlation and dependence between attributes.
• Density-based clustering
  In density-based clustering, clusters are defined as areas of higher density than the remainder of the data set. Objects in sparse areas - that are required to separate clusters - are usually considered to be noise and border points.

Features of cluster analysis

• Cluster analysis is mainly used in exploratory research. The results of the analysis can provide multiple possible solutions. The selection of the final solution requires the subjective judgment of the researcher and subsequent analysis;
• Regardless of whether there are actually different categories in the actual data, cluster analysis can be used to obtain solutions into several categories;
• The solution of cluster analysis depends entirely on the cluster variables selected by the researcher. Adding or deleting some variables may have a substantial impact on the final solution.
• Researchers should pay special attention to various factors that may affect the results when using cluster analysis.
• Outliers and special variables have a greater impact on clustering.