大数据背景下基于时间序列评价的淘宝交易数据挖掘

Pub Date : 2023-09-11 DOI:10.3233/idt-230111
Yanmin Zhang
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引用次数: 0

摘要

随着电子商务的出现,越来越多的人通过互联网进行交易,从而产生了大量的交易数据。数据挖掘就是根据数据规则对大量数据进行分解,分析网络交易数据,为企业分析市场、开展业务提供必要的数字环节。虽然时间序列数据挖掘比其他类型的数据挖掘要小,但它也是数据挖掘中的一个重要问题。在现实世界中,数据和时间之间的相关性非常普遍。时间序列模型的研究在数据挖掘中起着非常重要的作用。由于目的不同,淘宝数据分析也有所不同。除统计外,目前对淘宝数据的深入研究和分析相对不足,基于时间序列的淘宝交易数据分析较少。为了提高淘宝交易数据挖掘的准确性,更好地制定淘宝营销策略,本文采用时间序列数据挖掘技术对淘宝交易数据进行挖掘。本文首先介绍了淘宝交易数据挖掘的作用,然后描述了时间序列数据挖掘的计算方法,包括时间序列的重新描述和时间序列的相似性度量。最后,通过数据采集、数据处理、数据特征提取等一系列过程,建立了淘宝交易的数据挖掘模型,并提出了预测准确率和熵两个数据预测评价指标。实验部分验证了淘宝交易数据挖掘的效果。实验结果表明,该数据挖掘模型矩具有良好的数据预测精度和熵。平均数据预测准确率为94.26%,数据挖掘能力较强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Taobao transaction data mining based on time series evaluation under the background of big data
With the emergence of e-commerce, more and more people conduct transactions through the Internet, thus resulting in a large number of transaction data. Data mining is to decompose a large amount of data according to data rules, and analyze network transaction data, so as to provide necessary digital links for companies to analyze the market and develop business. Although time series data mining is smaller than other types of data mining, it is also an important issue of data mining. In the real world, the correlation between data and time is very common. The study of time series model plays a very important role in data mining. Due to different purposes, Taobao data analysis is also different. In addition to statistics, at present, the in-depth research and analysis of Taobao data are relatively insufficient, and the analysis of Taobao transaction data based on time series is rare. In order to improve the accuracy of Taobao transaction data mining and better formulate Taobao marketing strategy, this paper used time series data mining technology to mine Taobao transaction data. This paper first introduced the role of Taobao transaction data mining, and then described the calculation method of time series data mining, including the re-description of time series and the similarity measurement of time series. Finally, through a series of processes such as data collection, data processing and data feature extraction, the data mining model for Taobao transaction was established, and two data prediction evaluation indicators, namely prediction accuracy and entropy, were proposed. The experimental part verified the effect of Taobao transaction data mining. The experimental results showed that the data mining model moment had good data prediction accuracy and entropy. The average data prediction accuracy was 94.26%, and the data mining ability was strong.
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