K-means is a clustering (unsupervised learning) algorithm. The aim is to create homogeneous subgroups of examples. The individuals in the same subgroup are similar; the individuals in different subgroups are as different as possible.
The K-Means approach is already described in several tutorials (http://data-mining-tutorials.blogspot.com/search?q=k-means). The goal here is to compare its implementation with various free tools. We study the following tools: Tanagra 1.4.28; R 2.7.2 without additional package; Knime 1.3.5; Orange 1.0b2 and RapidMiner Community Edition.
Keywords: clustering, k-means, PCA, principal component analysis, MDS,multidimensional scaling
Components: PRINCIPAL COMPONENT ANALYSIS, K-MEANS, GROUP CHARACTERIZATION, EXPORT DATASET
Tutorial: en_Tanagra_et_les_autres_KMeans.pdf
Dataset: cars_dataset.zip
Reference:
D. Garson, "Cluster Analysis"
