WEKA数据解析实验.doc

WEKA 数据分析实验 实验简介 借助工具Weka 3.6 ，对数据样本进行测试，分类测试方法包括：朴素贝叶斯、决策树、随机数三类，聚类测试方法包括：DBScan，K均值两种； 数据样本 以熟悉数据分类的各类常用算法，以及了解Weka的使用方法为目的，本次试验中，采用的数据样本是Weka软件自带的“Vote”样本，如图： 关联规则 分析 操作步骤： 点击 “Explorer”按钮，弹出“Weka Explorer”控制界面 选择“Associate”选项卡； 点击“Choose”按钮，选择“Apriori”规则 点击参数文本框框，在参数选项卡设置参数如： 点击左侧“Start”按钮 执行结果： === Run information === Scheme: weka.associations.Apriori -I -N 10 -T 0 -C 0.9 -D 0.05 -U 1.0 -M 0.5 -S -1.0 -c -1 Relation: vote Instances: 435 Attributes: 17 handicapped-infants water-project-cost-sharing adoption-of-the-budget-resolution physician-fee-freeze el-salvador-aid religious-groups-in-schools anti-satellite-test-ban aid-to-nicaraguan-contras mx-missile immigration synfuels-corporation-cutback education-spending superfund-right-to-sue crime duty-free-exports export-administration-act-south-africa Class === Associator model (full training set) === Apriori ======= Minimum support: 0.5 (218 instances) Minimum metric confidence: 0.9 Number of cycles performed: 10 Generated sets of large itemsets: Size of set of large itemsets L(1): 12 Large Itemsets L(1): handicapped-infants=n 236 adoption-of-the-budget-resolution=y 253 physician-fee-freeze=n 247 religious-groups-in-schools=y 272 anti-satellite-test-ban=y 239 aid-to-nicaraguan-contras=y 242 synfuels-corporation-cutback=n 264 education-spending=n 233 crime=y 248 duty-free-exports=n 233 export-administration-act-south-africa=y 269 Class=democrat 267 Size of set of large itemsets L(2): 4 Large Itemsets L(2): adoption-of-the-budget-resolution=y physician-fee-freeze=n 219 adoption-of-the-budget-resolution=y Class=democrat 231 physician-fee-freeze=n Class=democrat 245 aid-to-nicaraguan-contras=y Class=democrat 218 Size of set of large itemsets