FFIMiner

class PAMI.fuzzyFrequentPattern.basic.FFIMiner.FFIMiner(iFile: str, minSup: float, sep: str = '\t')[source]

Bases: _fuzzyFrequentPatterns

About this algorithm

Description:

Fuzzy Frequent Pattern-Miner is desired to find all frequent fuzzy patterns which is on-trivial and challenging problem to its huge search space.we are using efficient pruning techniques to reduce the search space.

Reference:

Lin, Chun-Wei & Li, Ting & Fournier Viger, Philippe & Hong, Tzung-Pei. (2015). A fast Algorithm for mining fuzzy frequent itemsets. Journal of Intelligent & Fuzzy Systems. 29. 2373-2379. 10.3233/IFS-151936. https://www.researchgate.net/publication/286510908_A_fast_Algorithm_for_mining_fuzzy_frequent_itemSets

Parameters:

  • iFile (str) – Name of the Input file to mine complete set of correlated patterns

  • oFile (str) – Name of the output file to store complete set of correlated 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:

  • 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.

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

  • endTime (float) – To record the completion time of the mining process.

  • itemsCnt (int) – To record the number of fuzzy spatial itemSets generated.

  • mapItemSum (int) – To keep track of sum of Fuzzy Values of items.

  • joinsCnt (int) – * To keep track of the number of ffi-list that was constructed.*

  • BufferSize (int) – Represent the size of Buffer.

  • itemSetBuffer (list) – To keep track of items in buffer.

Methods:

  • mine() – Mining process will start from here.

  • getPatterns() – Complete set of patterns will be retrieved with this function.

  • save(oFile) – Complete set of frequent patterns will be loaded in to a output file.

  • getPatternsAsDataFrame() – Complete set of frequent patterns will be loaded in to a dataframe.

  • getMemoryUSS() – Total amount of USS memory consumed by the mining process will be retrieved from this function.

  • getMemoryRSS() – Total amount of RSS memory consumed by the mining process will be retrieved from this function.

  • getRuntime() – Total amount of runtime taken by the mining process will be retrieved from this function.

  • convert(value) – To convert the given user specified value.

  • compareItems(o1, o2) – A Function that sort all ffi-list in ascending order of Support.

  • FSFIMining(prefix, prefixLen, FSFIM, minSup) – Method generate ffi from prefix.

  • construct(px, py) – A function to construct Fuzzy itemSet from 2 fuzzy itemSets.

  • findElementWithTID(uList, tid) – To find element with same tid as given.

  • WriteOut(prefix, prefixLen, item, sumIUtil) – To Store the pattern.

Execution methods

Terminal command

Format:

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

Example Usage:

(.venv) $ python3  FFIMiner.py sampleTDB.txt output.txt 6

Note

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

Calling from a python program

from PAMI.fuzzyFrequentPattern import FFIMiner as alg

iFile = 'sampleTDB.txt'

minSup = 0.25 # can be specified between 0 and 1

obj = alg.CoMine(iFile, minSup, sep)

obj.mine()

fuzzyFrequentPattern = obj.getPatterns()

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

obj.save("outputFile")

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 B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

dfs(cands)[source]

Perform depth-first search (DFS) to find frequent patterns in a database.

This method recursively combines candidate patterns and calculates their support in the database, storing frequent patterns and their support counts.

Parameters:

cands (list) – List of candidate patterns represented as tuples.

Returns:

None

This method does not return anything explicitly, but it updates internal attributes _finalPatterns and _Database with frequent patterns and their support counts.

getMemoryRSS() → float[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() → float[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() → dict[source]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame[source]

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float[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]

Main() function start from here.

printResults() → None[source]

This function is used to print the results

save(outFile) → dict[source]

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

Parameters:

outFile (csv file) – name of the output file

Returns:

dictionary of frequent patterns

Return type:

dict

startMine()[source]

Code for the mining process will start from this function