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Analysis of Various Decision Tree Algorithms for Classification in Data Mining

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Bhumika Gupta, Aditya Rawat, Akshay Jain, Arpit Arora, Naresh Dhami

Bhumika Gupta, Aditya Rawat, Akshay Jain, Arpit Arora and Naresh Dhami. Analysis of Various Decision Tree Algorithms for Classification in Data Mining. International Journal of Computer Applications 163(8):15-19, April 2017. BibTeX

	author = {Bhumika Gupta and Aditya Rawat and Akshay Jain and Arpit Arora and Naresh Dhami},
	title = {Analysis of Various Decision Tree Algorithms for Classification in Data Mining},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2017},
	volume = {163},
	number = {8},
	month = {Apr},
	year = {2017},
	issn = {0975-8887},
	pages = {15-19},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913660},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Today the computer technology and computer network technology has developed so much and is still developing with pace.Thus, the amount of data in the information industry is getting higher day by day. This large amount of data can be helpful for analyzing and extracting useful knowledge from it. The hidden patterns of data are analyzed and then categorized into useful knowledge. This process is known as Data Mining. [4].Among the various data mining techniques, Decision Tree is also the popular one. Decision tree uses divide and conquer technique for the basic learning strategy. A decision tree is a flow chart-like structure in which each internal node represents a “test” on an attribute where each branch represents the outcome of the test and each leaf node represents a class label. This paper discusses various algorithms of the decision tree (ID3, C4.5, CART), their features, advantages, and disadvantages.


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Decision Tree, ID3, C4.5, Entropy, Information Gain.