Machine learning and data mining have established several effective applications in gene
selection analysis. This paper review semi-supervised learning algorithms and gene …

To improve on the robustness of traditional machine learning approaches, emphasis has
recently shifted to the integration of such methods with Deep Learning techniques. However …

In this article, we propose a general framework for the development of external evaluation
measures for soft clustering. Our proposal is based on the interpretation of soft clustering as …

In recent years, the research of statistical methods to analyze complex structures of data has
increased. In particular, a lot of attention has been focused on the interval-valued data. In a …

Supervised and unsupervised classification is crucial in many areas where different types of
data sets are common, such as biology, medicine, or industry, among others. A key …

Locating the centers before assigning clustering labels is a traditional routine of clustering
methods, which also limits the development of new clustering ideas. In this paper, we …

(1) Background: Cancer is a leading cause of death worldwide and each year,
approximately 400,000 children develop cancer. Early detection of cancer greatly increases …

Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang
(TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each …

Heterogeneous data is a dataset with various types including data type and data source.
Classification of heterogeneous data is still becoming a discussion in research in the field of …

The word" cancer" denotes to a set of syndromes that can spread to various bodily areas
and are brought on by abnormal cell proliferation. After cardiovascular illnesses, rendering …