数据挖掘技术在二手车交易系统中的应用

Application of Data Mining Technology in Second-hand Car Trading System

数据挖掘技术是指从数据集中发现有效的、新颖的、潜在有用的和最终可以理解模式的高级处理过程,FP-growth算法是数据挖掘算法的一种。FP-growth算法是一种基于FP-tree的频繁项集挖掘算法,此算法是将原始数据集压缩到一棵FP-tree上,对原始数据集进行两次扫描,挖掘过程不产生候选项集,不用候选测试的算法,它使用紧缩的数据结构,避免了对数据库的重复扫描,运算速度快。文中收集了乐购二手车交易平台2016年1月到2018年12月共3年的数据,系统中可供挖掘的模块包括:二手车信息模块,拍卖品管理模块,购物车管理模块,订单管理等信息模块。利用FP-growth算法对乐购二手车交易系统数据库中的车辆品牌、使用年限、车载人数、行驶里程、车辆价格、保养状况等信息进行整理、转换、对比、分析,从中发现二手车交易中的规律,挖掘用户购车和卖车的有关规律,提高了车辆的成交率。

Data mining technology is an advanced process that discovers effective,novel,potentially useful,and ultimately understandable patterns from data sets. As one of the data mining algorithms,FP-growth algorithm is a frequent item set mining algorithm based on FP-tree,which compresses the original data set onto a FP-tree and scans the original data set twice. The mining process neither generates a candidate set nor uses the candidate test algorithm. It uses a compact data structure,which avoids repeated scans of the database and has fast operation speed. We collect data from Tesco’s used car trading platform for three years from January 2016 to December 2018. The modules available for mining in the system include used car information module,auction management module,shopping cart management module,order management and other information modules. We adopt FP-growth algorithm to sort,convert,compare and analyze the information of the vehicle brand,service life,number of people on board,driving mileage,vehicle price and maintenance status in Tesco Used Vehicle Trading System database,from which we can find the rules of second-hand car transactions and excavate the relevant rules of buying and selling cars,to improve the turnover rate of vehicles.