ECLAT
- class PAMI.frequentPattern.basic.ECLAT.ECLAT(iFile, minSup, sep='\t')[source]
Bases:
_frequentPatterns- Description:
ECLAT 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 : Name of the Input file to mine complete set of frequent pattern’s
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:
- 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 ECLAT.py <inputFile> <outputFile> <minSup> Example Usage: (.venv) $ python3 ECLAT.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.ECLAT as alg obj = alg.ECLAT(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 Kundai under the supervision of Professor Rage Uday Kiran.
- 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