The Base Strategy for ID3 Algorithm of Data Mining Using Havrda and Charvat Entropy Based on Decision Tree
Keywords:
Data mining, decision tree, Shanon entropy, Havrda and Charvat entropy, ID3 algorithm, knowledge-driven decisionsAbstract
Data mining is used to extract the required data
from large databases [1]. The data mining algorithm is the mechanism that creates mining models [2]. To create a model, an algorithm first learns the rules from a set of data then looks for specific required patterns and trends according to those rules. The algorithm then uses the fallouts of this exploration to delineate the constraints of the mining model [2]. These constraints are then applied through the intact data set to extract the unlawful patterns and detailed statistics [2]. Decision-tree learning is one of the utmost efficacious erudition algorithms, due to its various eye-catching features: simplicity, comprehensibility, no parameters, and being able to handle mixed-type data [3]; ID3 is a simple decision tree erudition algorithm developed by Ross Quinlan (1983) [4]. This paper introduces the use of ID3 algorithm [4] of decision tree and we use Havrda and Charvat Entropy instead of Shannon Entropy [5]. By computing information we set particular property from
taken data as root of tree, also sub-root by repeating the process continually, to finally build the most optimized tree. This decision tree helps to take the decision for better analysis of data. Decision tree algorithm is used to select the best path to follow in the standard division. This paper introduces the use of
ID3 algorithm of decision tree. We are using Havrda and Charvat Entropy Instead of Shannon Entropy. This Decision Tree helps in taking the better decision to analyse the data.
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