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Survey Paper | Computer Science & Engineering | India | Volume 5 Issue 4, April 2016
A Survey on Decision Tree Algorithms of Classification in Data Mining
Himani Sharma | Sunil Kumar 
Abstract: As the computer technology and computer network technology are developing, the amount of data in information industry getting higher and higher. It is necessary to analyse this large amount of data and extract useful knowledge from it. Process of extracting the useful knowledge from huge set of incomplete, noisy, fuzzy and random data is called data mining. Decision tree classification technique is one of the most popular data mining technique. In decision tree divide and conquer technique is used as basic learning strategy. A decision tree is a structure that includes a root node, branches, and leaf nodes. Each internal node denotes a test on an attribut, each branch denotes the outcome of a test, and each leaf node holds a class label. The topmost node in the tree is the root node. This paper focus on the various algorithms of Decision tree (ID3, C4.5, CART), their characteristic, challenges, advantage and disadvantage.
Keywords: Decision Tree Learning, classification, C45, CART, ID3
Edition: Volume 5 Issue 4, April 2016,
Pages: 2094 - 2097
How to Cite this Article?
Himani Sharma, Sunil Kumar, "A Survey on Decision Tree Algorithms of Classification in Data Mining", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=NOV162954, Volume 5 Issue 4, April 2016, 2094 - 2097, #ijsrnet
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