In this research we applied classification models for prediction of students' performance, and
cluster models for grouping students based on their cognitive styles in e-learning …

Sales process disfunctions in the textile industry are problems that cause loss of customers,
incomplete market supply, etc. The objective of the research is to analyse transactions from …

Clustering is among the most popular data mining algorithm families. Before applying
clustering algorithms to datasets, it is usually necessary to preprocess the data properly …

Automated Machine Learning (AutoML) aims to identify the best-performing machine
learning algorithm along with its input parameters for a given dataset and a specific machine …

Class retrieval in gene expression microarray data analysis is highly challenging task.
Because of high class imbalance, highly dimensional feature space and small number of …

Rapid growth and storage of biomedical data enabled many opportunities for predictive
modeling and improvement of healthcare processes. On the other side analysis of such …

Increase in the number of documents in the corpuses like News groups, government
organizations, internet and digital libraries, have led to greater complexity in categorizing …

Most of the existing clustering algorithms are affected seriously by noise data and high cost
of time. In this paper, on the basis of CURE algorithm, a representative points clustering …

In this method, a non-class element based iterative clustering approach is discussed. First
the method extracts the terms of the class using the preprocessing algorithm. The …

Fourier transform infrared (FT‐IR) and horizontal attenuated total reflectance (HATR)
technique are used to obtain the FT‐IR spectra of the seed of green bristle grass (the seed …