# FrequentPatternMining Class for Mining Frequent Itemsets
#
# **Importing and Using the FrequentPatternMining Class in a Python Program**
#
# from geoanalytics.patternMining import FrequentPatternMining
#
# miner = FrequentPatternMining("data/input.txt")
#
# miner.run(minSupport=3)
#
__copyright__ = """
Copyright (C) 2022 Rage Uday Kiran
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
import pandas as pd
from PAMI.extras.dbStats.TransactionalDatabase import TransactionalDatabase
from PAMI.frequentPattern.basic import FPGrowth
from .abstract import PatternMiner
[docs]
class FrequentPatternMining(PatternMiner):
"""
**About this algorithm**
:**Description**:
This module implements the **FPGrowth algorithm** for mining **frequent itemsets** from transactional
datasets. Frequent itemset mining is a foundational data mining technique used to identify patterns
that occur frequently together in a database, and is widely used in market basket analysis, web usage
mining, and bioinformatics.
:**Parameters**:
- `inputFile` (*str*): Path to the input transactional database file.
:**Attributes**:
- **inputFile** (*str*): The transactional input file provided during object initialization.
- **miner** (*FPGrowth*): Instance of the FPGrowth algorithm from the PAMI library.
**Execution methods**
**Calling from a Python program**
.. code-block:: python
from geoanalytics.patternMining import FrequentPatternMining
miner = FrequentPatternMining("data/input.txt")
miner.run(minSupport=3)
**Credits**
Written by M. Charan Teja, under the guidance of Professor Rage Uday Kiran.
"""
def _create_database(self):
"""
Internal method to initialize the transactional database.
Returns:
TransactionalDatabase: Transactional database object from the PAMI library.
"""
return TransactionalDatabase(self.inputFile)
[docs]
def run(self, minSupport: int):
"""
Runs the FPGrowth algorithm to mine frequent itemsets.
Args:
minSupport (int): Minimum support threshold for identifying frequent patterns.
Output:
Prints the frequent itemsets to the console.
"""
self.miner = FPGrowth.FPGrowth(iFile=self.inputFile, minSup=minSupport)
self.miner.mine()
self.miner.printResults()