Apriori_Algorithm

This Python GUI application provides functionalities for processing data using the Apriori algorithm for association rule mining.

Features

File Selection: Users can select a CSV, TXT, or Excel file containing transactional data.
Data Configuration: Allows users to specify the percentage of data to use, minimum support, and minimum confidence for association rule mining.
Process Data Button: Executes the data processing and association rule mining based on user-defined configurations.
Output Display: Displays frequent itemsets, all combinations, and association rules in a scrollable text area.

The application preprocesses the data in the following steps:

Load Data: Loads transactional data from the selected file and groups transactions by transaction number.
Calculate Support Count: Calculates the support count for each individual item in the transactional data.
Apriori Algorithm: Applies the Apriori algorithm to find frequent itemsets based on the specified minimum support count.
Filtering: Filters out infrequent itemsets and retains only frequent itemsets with support count greater than or equal to the minimum support count.

Initialization: Initializes the GUI window with the specified title, size, and style configurations.
Widgets Creation: Creates various widgets including labels, entry fields, buttons, and a text area for displaying output.
File Browsing: Implements the functionality to browse and select a file using the browse_file method.
Data Processing: Executes data processing and association rule mining using the process_data method.
Output Display: Clears the output text area and displays frequent itemsets, all combinations, and association rules.

load_data: Loads transactional data from the selected file and groups transactions by transaction number.
calculate_support_count_item: Calculates the support count for each individual item in the transactional data.
apriori: Applies the Apriori algorithm to find frequent itemsets based on the specified minimum support count.
filter_by_support_count: Filters out infrequent itemsets based on the minimum support count.
generate_strong_association_rules: Generates strong association rules based on frequent itemsets and minimum confidence.
generate_all_combinations: Generates all possible combinations of items from frequent itemsets.

Creates an instance of the DataProcessorGUI class and starts the GUI application.