Abstract
Retraction Notice: Classification Optimization Clustering Model Simulation Based on User Interest
Highlights
R User classification [9, 11] is conducted in the mass data.The data in the classes is of similarity operation to get a de-A cision scheme, in which the similarity between discrepant data can be neglected, which is of great significance in reducing the operand of decision scheme and improving theD real time of decisions scheme
By weighting and increasing the interest parameters, the important attribute annotation is enhanced, the unimportant attribute annotation is reduced and the weight sum is converted into the sum of weight sum and interest parameter to not Considering the above user interest classification optimization results and integrating reasonable optimization conditions, the classification optimization self-adaptive fuzzy clustering objective function [7, 8] can be set as shown in equation (8) and two optimization constraints are given as shown in equation (9) and (10)
Cain( A) = I (s1, s2, sm ) E1 ( A). It greatly saves the time of rapid classification optimization of ID3 algorithm introducing user interest parameter because after optimization, ln(Skj / S j ) is replaced by m skj / S j to greatly simplify the complexity of user ink =1 terest classification and provide a reliable basis for building the classification optimization clustering model based on user interest [5]
Summary
R User classification [9, 11] is conducted in the mass data. A cision scheme, in which the similarity between discrepant data can be neglected, which is of great significance in reducing the operand of decision scheme and improving the. For building the classification optimization clustering model based on user interest [6]. The attribute leads to the minimum information quantity required by sample classification of user interest in the result decomposition [12]. CLASSIFICATION OPTIMIZATION CLUSTERING MODEL BASED ON USER INTEREST. Rapid Classification Optimization of ID3 Decision Tree Algorithm Integrating User Interest ( ) E( A) = v s1 j + + smj I j =1 s s1 j ,, smj (1). S1 j + + smj is the weight of j subset and equal to the ID3 decision tree algorithm is used to realize the classification optimization of user interest to provide a reliable basis number of user interest samples in the subset divides the central samples in S.
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