PAMI is a Python library containing 100+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)
weighted Frequent pattern mining aims to discover all interesting patterns in a transactional database that have support no less than the user-specified minimum support (minSup) constraint and weight no less than the user-specified minimum weight (minWeight). The minSup controls the minimum number of transactions that a pattern must appear in a database. The minWeight controls the minimum weight of item.
A transactional database is a collection of transactions, where each transaction contains a transaction-identifier and a set of items.
A hypothetical transactional database containing the items a, b, c, d, e, f, and g as shown below
| tid | Transactions |
|---|---|
| 1 | a c d f i m |
| 2 | a c d f m r |
| 3 | b d f m p r |
| 4 | b c f m p |
| 5 | c d f m r |
| 6 | d m r |
Note: Duplicate items must not exist in a transaction.
Each row in a transactional database must contain only items. The frequent pattern mining algorithms in PAMI implicitly assume the row number of a transaction as its transactional-identifier to reduce storage and processing costs. A sample transactional database, say WFIMSample.txt, is provided below.
a c d f i m
a c d f m r
b d f m p r
b c f m p
c d f m r
d m r
A weight database is a collection of items with their weights.
A hypothetical weight database, say WFIMWeightSample.txt, containing the items a, b, c, d, e, f, and g as shown below:
a 1.3
b 1.1
c 1.4
d 1.2
f 1.5
i 1.1
m 1.3
p 1.0
r 1.5
To understand about the database. The below code will give the detail about the transactional database.
The below sample code prints the statistical details of a database.
import PAMI.extras.dbStats.TransactionalDatabase as stats
obj = stats.TransactionalDatabase('WFIMSample.txt', ' ')
obj.run()
obj.printStats()
The input parameters to a frequent pattern mining algorithm are:
- String : E.g., ‘transactionalDatabase.txt’
- URL : E.g., https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/transactional_T10I4D100K.csv
- DataFrame with the header titled ‘Transactions’
- count (beween 0 to length of a database) or
- [0, 1]
- count (beween 0 to length of a database) or
- [0, 1]
The patterns discovered by a correlated pattern mining algorithm can be saved into a file or a data frame.
syntax: python3 algorithmName.py <path to the input file> <path to the output file> <path to the weight file> <minSup> <minWeight> <seperator>
Example: python3 WFIM.py inputFile.txt outputFile.txt weightSample.txt 3 2 ' '
import PAMI.weightedFrequentPattern.basic.WFIM as alg
iFile = 'WFIMSample.txt' #specify the input transactional database <br>
wFile = 'WFIMWeightSample.txt' #specify the input transactional database <br>
minSup = 3 #specify the minSupvalue <br>
minWeight = 1.2 #specify the minWeight value <br>
seperator = ' ' #specify the seperator. Default seperator is tab space. <br>
oFile = 'weightedPatterns.txt' #specify the output file name<br>
obj = alg.WFIM(iFile, wFile, minSup, minWeight, seperator) #initialize the algorithm <br>
obj.startMine() #start the mining process <br>
obj.savePatterns(oFile) #store the patterns in file <br>
df = obj.getPatternsAsDataFrame() #Get the patterns discovered into a dataframe <br>
obj.printStats() #Print the stats of mining process
The weightedPatterns.txt file contains the following patterns (format: pattern:support): !cat weightedPatterns.txt
!cat weightedPatterns.txt
r :4
r d :4
r d m :4
r m :4
c :4
c f :4
c f m :4
c m :4
f :5
f d :4
f d m :4
f m :5
d :5
d m :5
m :6
The dataframe containing the patterns is shown below:
df
| Patterns | Support | |
|---|---|---|
| 0 | r | 4 |
| 1 | r d | 4 |
| 2 | r d m | 4 |
| 3 | r m | 4 |
| 4 | c | 4 |
| 5 | c f | 4 |
| 6 | c f m | 4 |
| 7 | c m | 4 |
| 8 | f | 5 |
| 9 | f d | 4 |
| 10 | f d m | 4 |
| 11 | f m | 5 |
| 12 | d | 5 |
| 13 | d m | 5 |
| 14 | m | 6 |