A Design of Experiments Comparative Study on Clustering Methods

Abstract

Cluster analysis is a multivariate data mining technique that is widely used in several areas. It aims to group automatically the n elements of a database into k clusters, using only the information of the variables of each case. However, the accuracy of the final clusters depends on the clustering method used. In this paper, we present an evaluation of the performance of main methods for cluster analysis as Ward, K-means, and Self-Organizing Maps. Differently from many studies published in the area, we generated the datasets using the Design of Experiment (DOE) technique, in order to achieve reliable conclusions about the methods through the generalization of the different possible data structures. We considered the number of variables and clusters, dataset size, sample size, cluster overlapping, and the presence of outliers, as the DOE factors. The datasets were analyzed by each clustering method and the clustering partitions were compared by the Attribute Agreement Analysis, providing invaluable information about the effects of the considered factors individually and about their interactions. The results showed that, the number of clusters, overlapping, and the interaction between sample size and number of variable significantly affect all the studied methods. Moreover, it is possible to state that the methods have similar performances, with a significance level of 5%, and it is not possible to affirm that one outperforms the others. INDEX TERMS Clustering methods, design of experiments, K-means, self-organizing maps, ward.

Restoration of Data Structures Using Machine Learning Techniques

“…Each Design of experiments (DOE) comprises a series of steps: planning, execution of the experiment, and analysis of the collected experimental data using various statistical methods to draw valid and objective conclusions [33]. An experiment can be defined as a series of tests in which a set of input variables or factors (x) is changed by an experimenter in a controlled manner (c) to observe and identify how the response (y) of the system is affected by these changes [34].…”

“…An experiment can be defined as a series of tests in which a set of input variables or factors (x) is changed by an experimenter in a controlled manner (c) to observe and identify how the response (y) of the system is affected by these changes [34]. The experiments were performed systematically using factorial experiments (so-called factorial experimental designs/arrays), wherein several factors were altered during each experimental run to examine their impact of several factors and interactions on the response quantity [33]. A factorial experiment whose design consists of all possible combinations of chosen factors and levels is called fullfactorial design (FFD) [35].…”

A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation

“…Such methods have been applied in many engineering problems to improve the interpretation of extensive and correlated data. In fact, several studies already use multivariate strategies in a handful of applications, such as flux-cored arc welding process [2], moving average control chart [3], design of experiments on clustering methods [4] and applications in process monitoring [5,6]. Such approaches are also used in the energy [7], healthcare [8] and economy [9] sectors.…”