R Language Clustering Analysis

Clustering : Clustering refers to the analysis of grouping a collection of physical or abstract objects into multiple classes consisting of similar objects. It is a kind of important human behavior.
The goal of cluster analysis is to collect data on a similar basis to classify it. Clustering originates in many fields, including mathematics, computer science, statistics, biology and economics. In different applications, many clustering techniques have been developed that are used to describe data, to measure similarities between different data sources, and to classify data sources into different clusters.

Methods of Clustering:
1, according to the clustering results: covering type and non-covering type, if each data point is at least one class, then the cover cluster, otherwise non-covered cluster
2, according to the cluster variable type: Numerical, sub-type and mixed type
3, according to the principle of clustering: segmentation clustering, hierarchical clustering, density-based clustering and grid clustering

There are many ways to calculate the clustering distance between the numerical observation point and the observation point, there is a blog post "R language: Calculate various distances" specifically introduced, here is just a brief talk about what:
Euclidean distance (Euclidean): D=∑pi=1 (Xi−yi) 2−−−−−−−−−−−−√d=\sqrt{\sum_{i=1}^{p} (x_i-y_i) ^2}
Chebyshev distance (Chebychev): D=max (|xi−yi|) D=max (|x_i-y_i|)
Manhattan Distance (Manhattan): d=∑pi=1|xi−yi| D = \sum_{i=1}^{p} |x_{i}-y_{i}|
Minkowski distance (Minkowski): D=∑pi=1|xi−yi|k−−−−−−−−−−−−√k d= \sqrt[k]{\sum_{i=1}^{p} |x_{i}-y_{i}|^{k}}
Markov distance (Mahalanobis): d= (Xi−yi) ts−1 (xi−yi) −−−−