PAMI.frequentPattern.closed package

Submodules

PAMI.frequentPattern.closed.CHARM module

class PAMI.frequentPattern.closed.CHARM.CHARM(iFile, minSup, sep='\t')

Bases: _frequentPatterns

About this algorithm

Description:

CHARM is an algorithm to discover closed frequent patterns in a transactional database. Closed frequent patterns are patterns if there exists no superset that has the same support count as this original itemset. This algorithm employs depth-first search technique to find the complete set of closed frequent patterns in a transactional database.

Reference:

Mohammed J. Zaki and Ching-Jui Hsiao, CHARM: An Efficient Algorithm for Closed Itemset Mining, Proceedings of the 2002 SIAM, SDM. 2002, 457-473, https://doi.org/10.1137/1.9781611972726.27

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. 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:

• startTime (float) – To record the start time of the mining process.

• endTime (float) – To record the completion 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.

• mapSupport (Dictionary) – To maintain the information of item and their frequency.

• tree (class) – It represents the Tree class.

• itemSetCount (int) – It represents the total no of patterns.

• tidList (dict) – Stores the timestamps of an item.

• hashing (dict) – Stores the patterns with their support to check for the closed property.

Execution methods

Terminal command

Format:

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

Example Usage:

(.venv) $ python3 CHARM.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

from PAMI.frequentPattern.closed import CHARM as alg

iFile = 'sampleDB.txt'

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

obj = alg.CHARM(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

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

obj.savePatterns(oFile)

Df = obj.getPatternsAsDataFrame()

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 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()

Mining process will start from here by extracting the frequent patterns from the database. It performs prefix equivalence to generate the combinations and closed frequent patterns.

printResults()

This function is used to print the results

save(outFile)

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

Parameters:

outFile (csvfile) – name of the output file

startMine()

Mining process will start from here by extracting the frequent patterns from the database. It performs prefix equivalence to generate the combinations and closed frequent patterns.

PAMI.frequentPattern.closed.abstract module

Module contents