改进的 SLIQ 算法

能够有效地减少计算的复杂度，且算法不需要将所有属性的所有属性值的吉尼指数全部计

算，而是通过计算不同范围内的属性值就可以达到同样的效果。本文结合实际生活中的实

例，将该算法与原有 SLIQ 算法和基于人工神经网络的分类算法应用结果比较，实验结果

表明该算法的分类准确率远远高于 SLIQ 算法和基于人工神经网络的分类算法。-Improved SLIQ algorithm can effectively reduce the computational complexity, and Suanfa Buxuyaojiang all the attributes of all attributes of all the calculated values of the Gini index, but different range by calculating the value of the property to achieve the same effect. This combination of real life examples, the algorithm and the original SLIQ algorithm and artificial neural network classification algorithm application results, the experimental results show that the classification accuracy of the algorithm is much higher than SLIQ algorithm and the classification based on artificial neural network algorithm.