Comparing More than Two Agreement Measures Using Marginal Association

Comparing More Than Two Groups

Marginal association measures for clustered data.

The use of correlation coefficients in measuring the association between two continuous variables is common, but regular methods of calculating correlations have not been extended to the clustered data framework. For clustered data in which observations within a cluster may be correlated, regular inferential procedures for calculating marginal association between two variables can be biased. Th...

Comparing Two Measures for Formality

Formality is an important dimension of language style. Texts of different genres tend to have different degrees of formality. F-score (formality-score) is a most popular measure for formality to differ genres. It uses a method of combining proportions of words of different types, with nouns, adjectives, articles and prepositions as positive elements, and adverbs, verbs and interjections as nega...

Comparing Rule Measures for Predictive Association Rules

In this paper we study the predictive ability of some association rule measures typically used to assess descriptive interest. Such measures, namely conviction, lift and χ are compared with confidence, Laplace, mutual information, cosine, Jaccard and φ-coefficient. As prediction models, we use sets of association rules generated as such. Classification is done by selecting the best rule, or by ...

Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches

BACKGROUND We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on s...