Apriori

class PAMI.frequentPattern.basic.Apriori.Apriori(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database.

Reference:

Agrawal, R., Imieli ́nski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD. pp. 207–216 (1993), https://doi.org/10.1145/170035.170072

Parameters:

• iFile – str : Name of the Input file to mine complete set of frequent patterns

• oFile – str : Name of the output file to store complete set of frequent patterns

• minSup – int or float or str : The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. Otherwise, it will be treated as float.

• sep – str : This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

Attributes:

startTimefloat

To record the start time of the mining process

endTimefloat

To record the completion time of the mining process

finalPatternsdict

Storing the complete set of patterns in a dictionary variable

memoryUSSfloat

To store the total amount of USS memory consumed by the program

memoryRSSfloat

To store the total amount of RSS memory consumed by the program

Databaselist

To store the transactions of a database in list

Methods to execute code on terminal

Format:

(.venv) $ python3 Apriori.py <inputFile> <outputFile> <minSup>

Example Usage:

(.venv) $ python3 Apriori.py sampleDB.txt patterns.txt 10.0

Note

minSup will be considered in percentage of database transactions

Importing this algorithm into a python program

import PAMI.frequentPattern.basic.Apriori as alg

obj = alg.Apriori(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

obj.save(oFile)

Df = obj.getPatternInDataFrame()

memUSS = obj.getMemoryUSS()

print("Total Memory in USS:", memUSS)

memRSS = obj.getMemoryRSS()

print("Total Memory in RSS", memRSS)

run = obj.getRuntime()

print("Total ExecutionTime in seconds:", run)

Credits:

The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.

bitPacker(data, maxIndex)

getMemoryRSS() → float

Total amount of RSS memory consumed by the mining process will be retrieved from this function

Returns:

returning RSS memory consumed by the mining process

Return type:

float

getMemoryUSS() → float

Total amount of USS memory consumed by the mining process will be retrieved from this function

Returns:

returning USS memory consumed by the mining process

Return type:

float

getPatterns() → Dict[str, int]

Function to send the set of frequent patterns after completion of the mining process

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float

Calculating the total amount of runtime taken by the mining process

Returns:

returning total amount of runtime taken by the mining process

Return type:

float

mine() → None

Frequent pattern mining process will start from here

printResults() → None

This function is used to print the result

save(outFile) → None

Complete set of frequent patterns will be loaded in to an output file

Parameters:

outFile (csvfile) – name of the output file

Returns:

None

startMine() → None

Frequent pattern mining process will start from here