ECLATbitset

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

Bases: _frequentPatterns

About this algorithm

Description:

ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.

Reference:

Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): 372-390 (2000), https://ieeexplore.ieee.org/document/846291

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 ECLATbitset.py <inputFile> <outputFile> <minSup>

Example Usage:

(.venv) $ python3 ECLATbitset.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.ECLATbitset as alg

iFile = 'sampleDB.txt'

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

obj = alg.ECLATbitset(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 Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

getMemoryRSS()

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()

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()

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime()

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(memorySaver=True) → None

Frequent pattern mining process will start from here # Bitset implementation

printResults()

This function is used to print the result

save(outFile: str, seperator='\t') → None

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

Parameters:

• outFile (csvfile) – name of the output file

• seperator (string) – variable to store separator value

Returns:

None

startMine()

Frequent pattern mining process will start from here

We start with the scanning the itemSets and store the bitsets respectively. We form the combinations of single items and check with minSup condition to check the frequency of patterns