A tutorial on clustering algorithms-clustering knowledge

As the saying goes: There are a lot of classification problems in the natural sciences and social sciences. Generally speaking, a class refers to a set of similar elements. Clustering Analysis, also known as group analysis, is a statistical analysis method used to investigate classification issues (samples or indicators. Clustering Analysis originated from taxonomy. In ancient categorization, people mainly rely on experience and professional knowledge to classify data, and seldom use mathematical tools to classify data. With the development of Human Science and Technology, the requirements for classification are getting higher and higher, and sometimes it is difficult to accurately classify based on experience and professional knowledge. As a result, people gradually reference mathematical tools to classification, then, the multivariate analysis technology was introduced into the numerical classification to form a clustering analysis. Clustering Analysis is rich in content, including systematic clustering, ordered sample clustering, dynamic clustering, fuzzy clustering, graph theory clustering, and clustering prediction.

Today I found some tips for beginners: a tutorial on Clustering Algorithms

There is also a comprehensive toolkit: Open Source clustering software, also found a statistical software package, r language.

Turn: http://www.shamoxia.com/html/y2010/1621.html