Frequent Item Set Identification via Inverted Subset Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying frequent item sets, such as the Apriori algorithm, are inefficient and slow, especially when dealing with large datasets, as they start with individual items and gradually extend them to larger sets, whereas starting with item sets and identifying subsets can significantly enhance processing speed.
Innovation Solution
A method that determines the count of each item in multiple item sets, sorts these counts in ascending order, assigns identifiers, sorts identifiers in descending order, partitions item sets into groups, and sums counts of subsets to identify frequent item sets more efficiently by starting with item sets and then subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Apriori algorithm is used to identify frequent item sets by starting with individual items and extending them to larger sets, then the method can systematically explore all possible item sets, but the processing speed becomes slow and inefficient for large datasets
Solution Approach 1:
The patent inverts the traditional Apriori approach by starting with item sets rather than individual items. Instead of building up from single items to larger sets, the method begins with complete item sets and generates subsets, reversing the conventional bottom-up construction to achieve faster processing while maintaining completeness
Solution Approach 2:
The patent performs preliminary sorting of item sets by frequency count before generating subsets. By pre-sorting item sets in descending order of frequency, the algorithm can efficiently identify and process only the most frequent item sets first, reducing the search space and improving processing speed without compromising the identification of all frequent item sets
2Productivity
If the frequent pattern tree algorithm is used to sort items and build trees recursively, then processing speed improves, but the complexity of organizing and managing the tree structure increases
Solution Approach 1:
The patent extracts the tree building step from the frequent pattern tree algorithm and replaces it with a simpler subset generation approach. Instead of constructing complex recursive trees to represent item sets, the method directly generates subsets by removing elements from complete item sets, eliminating the need for complex tree structures while maintaining processing efficiency
Solution Approach 2:
The patent segments the item set generation process into two distinct phases: first sorting item sets by frequency, then generating subsets systematically. This segmentation separates the organizational complexity from the generation process, allowing efficient processing without requiring complex tree structures to manage the data
Data Source
AI summary
A system determines a count of each item in each item set, sorts each count into ascending order, assigns an ascending identifier to each item corresponding to each sorted count, and sorts each identifier in each item set in descending order. The system partitions item sets into a first group of item sets and a second group of item sets, each item set in the first group including a common largest identifier, determines a count for each subset of each item set of the first group, and determines a count of each subset of each item set by summing each count for each subset of each item set of the first group with each corresponding count for each corresponding subset of each item set of the second group. The system outputs a recommended item set based on the count of each subset of each item set.