数据分析实验
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WEKA 数据分析实验
1.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；
2.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：
3.关联规则分析
1)操作步骤：
a)点击“Explorer”按钮，弹出“Weka Explorer”控制界面
b)选择“Associate”选项卡；
c)点击“Choose”按钮，选择“Apriori”规则
d)点击参数文本框框，在参数选项卡设置参数如：
e)点击左侧“Start”按钮
2)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
Number of cycles performed: 10
Generated sets of large itemsets:
Size of set of large itemsetsL(1): 12
Large ItemsetsL(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 itemsetsL(2): 4
Large ItemsetsL(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 itemsetsL(3): 1
Large ItemsetsL(3):
adoption-of-the-budget-resolution=y physician-fee-freeze=n Class=democrat 219
Best rules found:
1. adoption-of-the-budget-resolution=y physician-fee-freeze=n 219 ==> Class=democrat 219 conf:(1)
2. physician-fee-freeze=n 247 ==> Class=democrat 245 conf:(0.99)
3. adoption-of-the-budget-resolution=y Class=democrat 231 ==> physician-fee-freeze=n 219 conf:(0.95)
4. Class=democrat 267 ==> physician-fee-freeze=n 245 conf:(0.92)
5. adoption-of-the-budget-resolution=y 253 ==> Class=democrat 231 conf:(0.91)
6. aid-to-nicaraguan-contras=y 242 ==> Class=democrat 218 conf:(0.9)
3)结果分析：
a)该样本数据，数据记录数435个，17个属性，进行了10轮测试
b)最小支持度为0.5，即至少需要218个实例；
c)最小置信度为0.9；
d)进行了10轮搜索，频繁1项集12个，频繁2项集4个，频繁3项集1个；
4.分类算法-随机树分析
1)操作步骤：
a)点击“Explorer”按钮，弹出“Weka Explorer”控制界面
b)选择“Classify ”选项卡；
c)点击“Choose”按钮，选择“trees” “RandomTree”规则