Sample value interval model optimization method and device

Abstract

The invention discloses an optimization method, device, electronic equipment and computer-readable medium for a sample value interval model, relates to the technical field of computer technology, can establish a sample value interval model, saves configuration and maintenance costs, and provides users with more reasonable samples Value interval configuration to improve user experience. A specific implementation of the method includes: obtaining a sample set of data; for each positive integer k in a given range, applying a clustering algorithm to cluster the sample set to generate k non-overlapping sub-sets of the sample set set, so as to obtain the corresponding sample value interval model, and when the sample value interval model satisfies the preset optimization objective, the sample value interval model is output.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to an optimization method, device, electronic equipment and computer-readable medium of a sample value interval model. Background technique [0002] With the development and application of big data technology, targeted screening and processing of big data is one of the key technologies of big data application. The diversification of price and price (i.e. sample value) causes the user to select the desired product, and must pass certain screening conditions in order to more accurately locate the desired product. Among them, screening products based on price is often usage instructions. [0003] In the course of realizing the present invention, the inventor finds that there are at least the following problems in the prior art: [0004] In the face of a huge number of samples, how to more accurately select the optimal sample set according to the needs is the key to technica...

AI Technical Summary

Problems solved by technology

For example, in an e-commerce website, sometimes there are a large number of products of the same category, and the unit price varies greatly. Users usually filter the required products according to the price range. The e-commerce website can establish the price range of the product (that is, the sample value range of the product) The model method screens and classifies commodities, but the classification and configuration of commodity price ranges are often manually configured and maintained by operators based on experience, which is time-consuming and laborious, and can easily lead to unreasonable price range configurations, and the data cannot be updated in time, making it difficult for users Quickly and accurately screen the required products to reduce user experience

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