Example 23.3: Cluster Analysis of Fisher Iris Data The iris data published by Fisher (1936) have been widely used for examples in discriminant analysis and cluster analysis. The sepal length, sepal ...

Except for extremely small data sets, it's not feasible to examine every possible clustering. For example, for a data set with only 50 tuples and three clusters, there are 3^50 / 3! possible ...

Example 23.1: Cluster Analysis of Flying Mileages between Ten American Cities. This first example clusters ten American cities based on the flying mileages between them. Six clustering methods are ...

For completely numeric data, the k-means clustering algorithm is simple and effective, especially if the k-means++ initialization technique is used. But non-numeric data (also called categorical data) ...

Clustering algorithms. Clustering algorithms are a form of unsupervised learning algorithm. With unsupervised learning, an algorithm is subjected to “unknown” data for which no previously ...

The authors evaluate clustering techniques using real data, and find that with sample sizes of less than 50, the reproducibility of results is poor. Article CAS PubMed PubMed Central Google Scholar ...