A Review on Data Mining Algorithms Based on Decision Trees: ID3 and C4.5
Sheenam1, Aanshi Bhardwaj2
1Student, Sheenam, Department of Computer Science, Lovely Professional University, Phagwara, India.
2Asst. Prof. Aanshi Bhardwaj, Department of Computer Science, Lovely Professional University, Phagwara, India
Manuscript received on May 01, 2016. | Revised Manuscript received on May 02, 2016. | Manuscript published on May 05, 2016. | PP: 55-59 | Volume-6 Issue-2, May 2016. | Retrieval Number: B2846056216
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data mining is a process of detection of valuable data (information) from massive data. It aids in exploring various patterns and rules from the given data. It is helpful for several purposes in private and public sectors. Many Industries use Data Mining for extract the valuable information from the large database to increase research, reduce price and enhance sales i.e. banking, medicine, insurance and retailing. Techniques of data mining are Association Rules, Classification, Clustering, Decision Trees. Classification is a process of classifying the data based on training set and class labels. It is a supervised learning technique. Decision Tree constructs a tree like structure that anticipates the target variable value. Each internal node of the tree represents the input variables. These variables are linked to child node based on the value of those variables. Last node of the tree is the leaf node which contains the value of the result of target variable based on the input values. The most commonly practiced decision tree algorithms are ID3 and C4.5.The intent of this study is to scrutinize these decision tree algorithms. At first we present concept of Data Mining, Classification and Decision Tree. Then we present ID3 and C4.5 algorithms and we will make comparison of these two algorithms.
Keywords: Classifiacation, Data Mining, Decision Tree, Traffic-accident.