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In this repository implemente 6 class of Association rule data mining algorithm
1.Apriori (apriori.py)
apriori algorithm
2.Apriori_compress(apriori_compress.py)
transaction compression processing for apriori algorithm
3.Apriori_hash(apriori_hash.py)
hash method for apriori algorithm
4.Apriori_plus(apriori_plus.py)
transaction compress + dataset compress+hash + apriori
5.Fp_growth(fp_growth.py)
fp-growth algorithm
6.Fp_growth_plus(fp_growth_plus.py)
dataset compress + fp_growth
- running progress
- the result of association rule data mining
- download the repository
git clone https://github.com/blackAndrechen/data_mine
- into this folder
cd data_mine
- write your own code,take apriori algorithm for example
from apriori import *
data=[[l1,l2,l3,l4],
[l1,l3,l5],
[l1,l3,l4]]
min_support=2
min_confident=0.6
apr=Apriori()
rule_list=apr.generate_R(data,min_support,min_confident)
- if you want use others algorithm,the use method is same,for example
from fp_growth import *
fp=Fp_growth()
rule_list=fp.generate_R(data,min_support,min_confident)
- in my code ,i use
groceries.csvand药方.xlsdata file,you can try running it
filename="groceries.csv"
min_support=25
min_conf=0.7
# filename="药方.xls"
# min_support=600
# min_conf=0.9
import os
current_path=os.getcwd()
path=current_path+"/dataset/"+filename
#path='/home/czpchen/文档/github/data_mine/dataset/groceries.csv'
data=load_data(path)
apr=Apriori()
rule_list=apr.generate_R(data,min_support,min_conf)
- if you want use youself dataset,suggest you rewrite a function to read youself dataset,And make sure your data set looks like this.
data=[[l1,l2,l3,l4],
[l1,l3,l5],
[l1,l3,l4]]
- if you want save the result of Association rule data
save_path=save_path=current_path+"/log/"+filename.split(".")[0]+"_apriori.txt"
#save_path='/home/czpchen/文档/github/data_mine/log/groceries_apriori.txt'
save_rule(rule_list,save_path)
simple analyse of my dataset
