Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that gathers similar objects into groups called the clusters. The endpoint is a set of clusters, where each cluster is distinct from other clusters, and the objects within each cluster are broadly similar to each other.
Agglomerative: This approach is also known as the bottom-up approach: each instance starts in its own cluster, and pairs of clusters are merged as one move up the hierarchy. In this method, we start with each object forming a separate group. It continues to merging the groups or objects that are close to one another. It keeps on doing so until all of the objects are merged into one or until the condition of termination happens.
Divisive: This approach is also known as the top-down approach: all observations start from one cluster, and splits are carried out recursively as one moves down the hierarchy. In this method, we start with all of the objects in the same cluster. In the process of iteration, a cluster is divided into smaller clusters. It is down until each cluster with only one object or the condition of termination happens.
|Identify the two clusters that are closest together|
|Merge the two most similar clusters. This iterative process continues until all the clusters are merged together.|
|Project name||Hierarchical Clustering Service|
|Samples requriements||Hierarchical clustering can be performed with either a distance matrix or raw data. When raw data is provided, the software will automatically compute a distance matrix in the background. The distance matrix below shows the distance between six objects.|
|Detection cycle||3-5 days.|
|Service including||We provide you with raw data and calculation result analysis service.|
ComputaBio provides corresponding analysis services. The goal of cluster analysis is to collect data for classification on a similar basis. Clustering originates from many fields, including mathematics, computer science, statistics, biology, and economics. In different application fields, many clustering technologies have been developed. These technical methods are used to describe data, measure the similarity between different data sources, and classify data sources into different clusters. If you have needs in this regard, please feel free to contact us.