Traditional clustering algorithms for medical image segmentation can only achieve
satisfactory clustering performance under relatively ideal conditions, in which there is …

The k-means algorithm is a widely used method that starts with an initial partitioning of the
data and then iteratively converges towards the local solution by reducing the Sum of …

This paper presents an Artificial Bee Colony (ABC) optimization based algorithm for co-
clustering of high-dimensional data. The ABC algorithm is used for optimization problems …

This paper presents a new semantic kernel for classification of high-dimensional data in the
framework of Support Vector Machines (SVM). SVMs have gained widespread application …

Microplastic contamination presents a significant global environmental threat, yet scientific
understanding of its morphological distribution within ecosystems remains limited. This study …

Microarray technology is a powerful tool that enables simultaneous analysis of the
expression level of a large number of genes for different samples. Reliable information on …

Co-clustering refers to the simultaneous clustering of objects and their features. It is used as
a clustering technique when the data exhibit similarities only in a subset of features instead …

This paper presents an exhaustive empirical study to identify biomarkers using two
approaches: frequency-based and network-based, over 17 different biclustering algorithms …

Co-clustering is to cluster samples and features simultaneously, which can also reveal the
relationship between row clusters and column clusters. Therefore, lots of scientists have …

Probabilistic graphs have gained much interest in the data mining community since the big
data revolution. Graph clustering is a widely used technique in exploratory data analysis …