在Mahout中实现Apriori算法的步骤如下：

导入必要的库和函数：

import org.apache.mahout.fpm.pfpgrowth.fpgrowth2.FPGrowth;import org.apache.mahout.fpm.pfpgrowth.fpgrowth2.FPGrowthItemsets;import org.apache.mahout.fpm.pfpgrowth.fpgrowth2.FPGrowthJob;import org.apache.mahout.fpm.pfpgrowth.fpgrowth2.FPGrowthDriver;

创建一个FPGrowth对象并设置参数：

FPGrowth fpGrowth = new FPGrowth();fpGrowth.setMinSupport(0.5);fpGrowth.setNumGroups(50);

读取数据集并进行格式转换：

FPGrowthDriver.runFPGrowth(args, fpGrowth);

运行Apriori算法并获取频繁项集：

FPGrowthJob fpGrowthJob = new FPGrowthJob();FPGrowthItemsets itemsets = fpGrowthJob.findFrequentItemsets(data, fpGrowth, true, false);

输出频繁项集：

for (FPGrowthItem item : itemsets.all()) { System.out.println(item);}

通过以上步骤，就可以在Mahout中实现Apriori算法并获取频繁项集。需要注意的是，在实际应用中，还需要根据具体数据集和需求调整参数和设置。