Determining optimal number of clusters in a dataset is a challenging task. Though some methods are available, there is no algorithm that produces unique clustering solution. The paper proposes an Automatic Merging for Single Optimal Solution (AMSOS) which aims to generate unique and nearly optimal clusters for the given datasets automatically. The AMSOS is iteratively merges the closest cluster...

Assume we are given a large collection of objects, each with several hundred attributes, and we wish to assign scores and take appropriate actions for each object in such a way as to maximize a given objective function defined on the entire collection. In this paper, we describe a methodology that uses data mining to divide the objects into different clusters on the basis of their attributes so...

Clustering validity indices are methods for examining and assessing the quality of data clustering results. Various studies provide a thorough evaluation of their performance using both synthetic and real-world datasets. In this work, we describe various approaches to the topic of evaluation of a clustering scheme. Moreover, a new solution to a problem of selecting an appropriate clustering val...

Variable string length genetic algorithm (GA) is used for developing a novel nonparametric clustering technique when the number of clusters is not fixed a priori. Chromosomes in the same population may now have different lengths since they encode different number of clusters. The crossover operator is redefined to tackle the concept of variable string length. Cluster validity index is used as a...

Social anxiety is an emotional disorder common to various populations around the world. The newly developed Self-Beliefs Related to Social Anxiety Scale (SBSA) aims to assess three kinds of self-beliefs through 15 items that include self-related cognitive factors that evidently result in social anxiety. This study explored the psychometric characteristics of SBSA among 978 Chinese. An eight-ite...

BACKGROUND The development of a patient-reported outcome measure (PROM) for polymyalgia rheumatica (PMR), a condition that causes pain, stiffness and disability, is necessary as there is no current validated disease-specific measure. Initial literature synthesis and qualitative research established a conceptual framework for the condition along with a list of symptoms and effects of PMR that pa...

Ensemble methods are known to increase the performance of learning algorithms, both on supervised and unsupervised learning. Boosting algorithms are quite successful in supervised ensemble methods. These algorithms build incrementally an ensemble of classifiers by focusing on objects previously misclassified while training the current classifier. In this paper we propose an extension to the Evi...