Browsing Department of Computer Science and Engineering (CSE) by Subject "Clustering"

Now showing items 1-5 of 5

    • Active Learning with Clustering for Mining Big Data 

      Ibrahim, Md.; Masud, Salman; Rabby, Reza E (2019-05-28)
      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 ...

    • An Adaptive Ensemble Classifier for Mining Concept-Drifting Data Streams 

      Rahman, Chowdhury Mofizur; Farid, Dewan Md; Zhang, Li; Hossain, Alamgir; Strachan, Rebecca; Sexton, Graham; Dahal, Keshav (Expert Systems with Applications, 2013-11-01)
      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 ...

    • Classification by Clustering (CbC): An Approach of Classifying Big Data based on Similarities 

      Khan, Sakib Shahriar; Ahamed, Shakim; Jannat, Miftahul; Monwar, Irin (2019-01-30)
      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 ...

    • Enhanced Classification Accuracy on Naive Bayes Data Mining Models 

      Rahman, Chowdhury Mofizur; Kabir, Md. Faisal; Hossain, Alamgir; Dahal, Keshav (International Journal of Computer Applications, 2011-08-01)
      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 ...

    • Solving Multi-Class Classification Tasks with Classifier Ensemble based on Clustering 

      Haque, Mohammad Rafiul; Saud, Alam Al; Annajiat Yasmin, Bipasha; Hossain, Sabbir (2019-09-07)
      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 ...