Traditional data mining techniques cannot be directly applied to the real-time data streaming environment. Existing mining classifiers therefore need to be updated frequently to adopt the changes in data streams. In this ...

A classification paradigm is a data mining framework containing all the concepts extracted from the training dataset to differentiate one class from other classes existed in data. The primary goal of the classification ...

Data classification in supervised learning is the process of classifying data for data mining task that helps to analyses data for decision making. The objective of a classification model is to correctly predict the ...

Big data mining is become a key research issue nowadays. It's costly and also time-consuming to extract knowledge from big data. Big data is so big, it contains millions of data points that's why it's very difficult to ...

Ensemble learning is very popular for few decades for solving classification problems, because it generates and combines a diversity of classifiers using the same learning algorithm for the base-classifiers. In this paper ...