Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Big data is raising many technical challenges in today's world like the amount of data is getting higher and higher, streaming data and the data dimensionality which affects academic researches and IT sectors. This was found that streaming data accumulates exponentially making traditional methods to become infeasible during real-time data mining and also to extract useful knowledge from it. Generally, for the purpose of decision making, decision trees are used for information gaining. But, due to certain drawbacks, some new approach needs to be used. A learning algorithm is to be implemented for mining streaming data which is more precise. By using Random Forest algorithm for classification which achieves enhanced analytical accuracy within reasonable processing time against streaming data. In this survey, we had explained most commonly used decision tree algorithms like CHAID, CART, C4.5, ID3 and also Random Forest algorithm.
Keywords:
Decision tree, ID3, C4.5, CART, CHAID, and Random Forest
Cite Article:
"Decision Tree Classifier For Mining Data Stream: A Survey", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.4, Issue 1, page no.103 - 106, January-2019, Available :http://www.ijrti.org/papers/IJRTI1901017.pdf
Downloads:
000202639
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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