A summary: They use the bag-of-words model with stopwords and stemming, and compute document vectors, where each word is a dimension, and the weight is the TF-I

A summary: They use the bag-of-words model with stopwords and stemming, and compute document vectors, where each word is a dimension, and the weight is the TF-IDF value for that word. The metrics covered: Euclidean distance, cosine similarity, Jaccard coefficient, Pearson correlation coefficient, averaged Kullback-Leibler divergence. They use these metrics with K-means clustering, and evaluate each on purity and entropy of the generated clusters. The purity measure evaluates the coherence of a cluster, that is, the degree to which a cluster contains documents from a single category. The entropy measure evaluates the distribution of categories in a given cluster. In general, the smaller the entropy value, the better the quality of the cluster is.