Loyalty index prediction method based on improved nearest neighbor algorithm

Abstract

The invention discloses a loyalty index prediction method based on an improved nearest neighbor algorithm. The method comprises steps that during loyalty index classification prediction, firstly, a Bayes algorithm is utilized to pre-process a data set, non-loyal clients and loyal clients are screened out, and the loyal clients include clients with high loyalty indexes and client with low loyalty indexes; the loyal clients are further classified through utilizing the nearest neighbor algorithm, the clients with the high loyalty indexes are acquired, and loyalty index prediction is accomplished. The method is advantaged in that influence of a K value on the nearest neighbor algorithm is reduced, memory cost is reduced, the obvious time advantage is realized, and loyalty index classification accuracy is improved.

Description

technical field [0001] The invention relates to the field of data mining classification prediction, in particular to a loyalty prediction method based on an improved nearest neighbor algorithm. Background technique [0002] The purpose of classification is to predict an outcome for any given test instance. Given a test instance that does not have the same set of attributes in the training set, the algorithm should be able to correctly predict the class label for that test instance. The accuracy of the predictions determines how good the algorithm is. Often a single classification technique like Bayesian, decision tree, K nearest neighbors etc. is used to predict the class of new instances. [0003] Predicting customer loyalty based on classification technology is currently a research and development hotspot for large Internet giants. For example, Zhang Yongli (Zhang Yongli. Prediction and implementation of customer loyalty based on Data Mining [J]. brand, 2011 (2): 15-16)...

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Problems solved by technology

For example, Zhang Yongli (Zhang Yongli. Prediction and implementation of customer loyalty based on Data Mining [J]. brand, 2011 (2): 15-16) proposed to use neural network algorithm to evaluate and predict customer loyalty, and achieved good results , but the model structure is difficult to determine and it is easy to cause a decline in generalization ability; Tian Hui (Tian Hui. Research on the application of data mining in the field of automobile sales CRM[D]. Zhejiang, China: College of Computer Science and Technology, Zhejiang University of Technolog, 2012) combined the classification decision tree (C4.5) algorithm and the KNN algorithm to propose a loyalty prediction model. When the calculation cost is large, the effect of the algorithm will become unsatisfactory, and the number of neighbors to be selected depends on K value is difficult to determine; Liu Pengfei (Liu Pengfei. Research and system implementation of customer loyalty prediction model [D]. Jilin, China: College of Computer Science and Technology, Jilin University, 2011) proposed to establish customer loyalty prediction based on Bayesian algorithm The model can effectively predict the loyalty of each telecom customer, but it cannot handle the changes that occur when the characteristics match, and the supplementary effect on missing data will affect the accuracy

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