Privacy-Preserving Data Mining with Random decision tree framework
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2017, Vol 19, Issue 4
Abstract
Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data mining algorithms available for classification based on Artificial Neural Network, Nearest Neighbour Rule & Bayesian classifiers but decision tree mining is simple one. ID3 and C4.5 algorithms have been introduced by J.R Quinlan which produce reasonable decision trees. The objective of this paper is to present these algorithms. At first we present the classical algorithm that is ID3, then highlights of this study we will discuss in more detail C4.5 this one is a natural extension of the ID3 algorithm. And we will make a comparison between these two algorithms and others algorithms such as C5.0 and CART. Indexterms: Data mining, Privacy-preserving data mining, classification horizontal partitioning, vertical partitioning
Authors and Affiliations
Ms. Ch. Likitha Sravya, Mrs. G. V Rajya Lakshmi
Keywords
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- EP ID EP385559
- DOI 10.9790/0661-1904034349.
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