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- W2365963419 abstract "To solve the problem of churn prediction in telecom, this paper proposes a k-nearest neighbors classification method based on time-series pattern matching. This method integrates time-series pattern matching and k-nearest neighbor classification to predict the class of unclassified data. Compared with the conventional prediction methods (such as decision-tree based method), the method can make prediction without discrete transition for time-series. This paper describes in detail the design and the application to the real telecommunication data of the algorithm. The comparison of experimental results between the method of this paper and C4.5 indicates that the proposed method preserves the integrity of time series and can improve the accuracy of the prediction." @default.
- W2365963419 created "2016-06-24" @default.
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- W2365963419 date "2006-01-01" @default.
- W2365963419 modified "2023-09-25" @default.
- W2365963419 title "Application of k-nearest Neighbors Classification Based on Time-series Pattern Matching in Churn Prediction" @default.
- W2365963419 hasPublicationYear "2006" @default.
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