Decision‐making method using a visual approach for cluster analysis problems; indicative classification algorithms and grouping scope

Abstract

Currently, classifying samples into a fixed number of clusters (i.e. supervised cluster analysis) as well as unsupervised cluster analysis are limited in their ability to support 'cross-algorithms' analysis. It is well known that each cluster analysis algorithm yields different results (i.e. a different classification); even running the same algorithm with two different similarity measures commonly yields different results. Researchers usually choose the preferred algorithm and similarity measure according to analysis objectives and data set features, but they have neither a formal method nor tool that supports comparisons and evaluations of the different classifications that result from the diverse algorithms. Current research development and prototype decisions support a methodology based upon formal quantitative measures and a visual approach, enabling presentation, comparison and evaluation of multiple classification suggestions resulting from diverse algorithms. This methodology and tool were used in two basic scenarios: (I) a classification problem in which a 'true result' is known, using the Fisher iris data set; (II) a classification problem in which there is no 'true result' to compare with. In this case, we used a small data set from a user profile study (a study that tries to relate users to a set of stereotypes based on sociological aspects and interests). In each scenario, ten diverse algorithms were executed. The suggested methodology and decision support system produced a cross-algorithms presentation; all ten resultant classifications are presented together in a 'Tetris-like' format. Each column represents a specific classification algorithm, each line represents a specific sample, and formal quantitative measures analyse the 'Tetris blocks', arranging them according to their best structures, i.e. best classification.

Mapping world cultures: Cluster formation, sources and implications

“…Second, the higher Global level seems equivalent to the 1985 map, which, excluding independent countries, comprised eight clusters. Third, another analysis using the multi-algorithm voting method (Bittman & Gelbard, 2007 further supports that 11 is the preferred number of clusters for our dataset. 9 Relative adjacency is inferred by inter-cluster congruency level: for example, the Confucian and Far Eastern Global clusters merge at a height of 0.79 in the dendrogram, indicating that they are closer to each other than to any other Global cluster.…”

Lifestyle risk factors of students: A cluster analytical approach

“…This procedure is ideal for large amounts of data (e.g., N 200), is applicable to both categorical and continuous data, and is able to automatically determine the optimal number of clusters for a given data set (Bittmann and Gelbard, 2007). Cluster analysis is a technique of identifying subgroups of cases in a specific population based on shared characteristics.…”

Cluster analysis using multi-algorithm voting in cross-cultural studies

“…To overcome these shortcomings, we used the method developed by Bittmann and Gelbard (2007) known as Multi-Algorithms Voting. Multi-Algorithms Voting is a decision support methodology for a cross-algorithm presentation in which all clusters are presented together in a ''tetris-like format".…”

“…Ward: This method calculates the centroid for each cluster and the square of the likelihood measure of each sample in both the cluster and the centroid. The two clusters, which when united have the smallest (negative) effect on the sum of likelihood measures, are the clusters that need to be united (Bittmann & Gelbard, 2007).…”

“…MAV is a decision support methodology that enables presentation, comparison and evaluation of multi-algorithm outputs derived from widely diverse algorithms (Bittmann & Gelbard, 2007). In part one, we provide a formal and quantitative method for determining the number of clusters, thus avoiding arbitrary decisions.…”