A Comparative study of Data stream classification using Decision tree and Novel class Detection Techniques
Mistry Vinay,  Ms.Astha Baxi
The rapid development in the e-commerce and distributed computing generates millions of the transaction,
continuously. This continues arrival of data is considered as a DataStream. Data mining process for classification needs
considerable modification to cope with continuous data. As Mining continues stream of data, conceptually has infinite
length, and the class of data may change in sudden or gradual or hike, for which classification model is completely unknown
or not prepared. Here investigation is made on different techniques proposed for the data stream classification using
decision trees. Different approaches of decision tree classification for the stream data are analyzed & compared. The
primary comparison parameters are time and accuracy. Also shown efforts made for handling the change in the concept and
they are compared in terms of memory, technique and accuracy.
Keywords- Data stream, Novel class, Incremental learning, Ensemble Technique, Decision tree, Concept drift
Cite this Article
Mistry Vinay,  Ms.Astha Baxi,   "A Comparative study of Data stream classification using Decision tree and Novel class Detection Techniques"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 2, pp.1844-1849, June 2014, Available at :http://www.ijedr.org/papers/IJEDR1402090.pdf