ECLATDiffset

class PAMI.frequentPattern.basic.ECLATDiffset.ECLATDiffset(iFile, minSup, sep='\t')[source]

Bases: _frequentPatterns

About this algorithm

Description:

ECLATDiffset uses diffset to extract the frequent patterns in a transactional database.

Reference:

KDD ‘03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining August 2003 Pages 326–335 https://doi.org/10.1145/956750.956788

Parameters:

iFile (str or URL or dataFrame) – 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.
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:

startTime (float) – To record the start time of the mining process.
endTime (float) – To record the end time of the mining process.
finalPatterns (dict) – Storing the complete set of patterns in a dictionary variable.
memoryUSS (float) – To store the total amount of USS memory consumed by the program.
memoryRSS (float) – To store the total amount of RSS memory consumed by the program.
Database (list) – To store the transactions of a database in list.

Execution methods

Terminal command

Format:

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

Example Usage:

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

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

import PAMI.frequentPattern.basic.ECLATDiffset as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

obj = alg.ECLATDiffset(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

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

obj.savePatterns(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 Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

getMemoryRSS()[source]

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()[source]

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()[source]

This function returns the frequent patterns after completion of the mining process

Returns:: returning frequent patterns
Return type:: dict

getPatternsAsDataFrame()[source]

Storing final frequent patterns in a dataframe.

Returns:: returning frequent patterns in a dataframe
Return type:: pd.DataFrame

getRuntime()[source]

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()[source]: Frequent pattern mining process will start from here

printResults()[source]: This function is used to print the results.

save(outFile: str, seperator='\t') → None[source]

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

Parameters:

outFile (csvfile) – name of the output file
seperator (string) – variable to store separator value

Returns:

None

startMine()[source]: Frequent pattern mining process will start from here