Study on the application of data mining for customer groups based on the modified ID3 algorithm in the e-commerce

Customer information-mining in e-commerce is very important. ID3 algorithm is a mining one based on decision tree, which selects property value with the highest gains as the test attribute of its sample sets, establishes decision-making node, and divides them in turn. ID3 algorithm involves repeated logarithm operation, and it will affect the efficiency of generating decision tree when there are a large number of data, so one must change the selection criteria of data set attributes, using the Taylor formula to transform the algorithm to reduce the amount of data calculation and the generation time of decision trees and thus improve the efficiency of the decision tree classifier. It is shown that the use of improved ID3 algorithm to deal with the customer base data samples can reduce the computational cost, and improve the efficiency of the decision tree generation.