Quick and accurate medical diagnoses are crucial for the successful treatment of diseases.
Using machine learning algorithms and based on laboratory blood test results, we have built …

In expert systems, data mining methods are algorithms that simulate humans' problem-
solving capabilities. Clustering methods as unsupervised machine learning methods are …

A clustering ensemble aims to combine multiple clustering models to produce a better result
than that of the individual clustering algorithms in terms of consistency and quality. In this …

Many clustering algorithms, including cluster ensembles, rely on a random component.
Stability of the results across different runs is considered to be an asset of the algorithm. The …

This paper studies the ensemble selection problem for unsupervised learning. Given a large
library of different clustering solutions, our goal is to select a subset of solutions to form a …

With the increasing number of samples, the manual clustering of COVID-19 and medical
disease data samples becomes time-consuming and requires highly skilled labour …

Adjusted Rand index is used to measure diversity in cluster ensembles and a diversity
measure is subsequently proposed. Although the measure was found to be related to the …

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-
posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the …

Assessing the similarity between cases is a key aspect of the retrieval phase in case-based
reasoning (CBR). In most CBR work, similarity is assessed based on feature value …

Abundant data of the patients is recorded within the health care system. During data mining
process, we can achieve useful knowledge and hidden patterns within the data and …