Clustering attempts to discover significant groups present in a data set. It is an unsupervised process. It is difficult to define when a clustering result is acceptable. Thus, several clustering validity indices are developed to evaluate the quality of clustering algorithms results. In this paper, we propose to improve the quality of a clustering algorithm called ”CLUSTER” by using a validity ...

Different clustering algorithms achieve different results to certain data sets because most clustering algorithms are sensitive to the input parameters and the structure of data sets. Cluster validity, as the way of evaluating the result of the clustering algorithms, is one of the problems in cluster analysis. In this paper, we build up a framework for cluster validity process, meanwhile a sum-...

Background: Patients’ trust in their physicians can affect therapeutic outcomes. Measurement of patient’s trust levels is a helpful approach for policymakers in healthcare systems. Aim: The present study was targeted toward the translation and psychometric assessment of patients’ trust in midwifery care questionnaire. Method: This cross-sectional study was conducted on 210 female patients refer...

Recently, clustering algorithms combined conventional methods and artificial intelligence. FSCSOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster ...

Data clustering describes a set of frequently employed techniques in exploratory data analysis to extract "natural" group structure in data. Such groupings need to be validated to separate the signal in the data from spurious structure. In this context, finding an appropriate number of clusters is a particularly important model selection question. We introduce a measure of cluster stability to ...