BibTeX:

@article{IJIRSTV4I1041,
     title={Complex Class Classification for Gradually Novel Classes in Data Stream Mining},
     author={Prashant Gore},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={4},
     number={1},
     pages={147--152},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV4I1041.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. Now a day’s huge amount of data is processed & analyzed. So it is very important to classify data & information properly. The information is basically unstructured & continuous. So huge volume of continuous data which has multidimensional feature & often fast changing. It is required to construct model which adapt such changes & give fast response. Such information flow examples are network traffic, sensor data, call center records etc. Class evolution is now a day’s important topic in data stream mining which handles such data. So in previous work proposed a model Class Based ensemble for Class evolution (CBCE) to maintain such a large amount of streams. But for complex & massive data result would be different. So complex class ensemble model (CCEM) is proposed for classification so huge & complex classes can be handled & classify & also proposed a model for class disappearance only so that more emphasize on class disappearance than class reoccurrence & novel class.

Data Stream Mining, Class Evolution, Ensemble Model, Incremental Learning