Frequent Itemset Mining for High Value Categorical Segments
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Solution Overview
Problem
Current systems for segmenting categorical data face inefficiencies in finding high value segments due to the combinatorial explosion of attribute combinations, and existing algorithms struggle to simultaneously consider value metrics and attribute values, leading to ineffective and non-distinct segment identification.
Innovation Solution
The system employs a Frequent Itemset Mining (FIM) algorithm that applies categorical constraints, thresholding metrics, and similarity metrics simultaneously to identify high value segments, optimizing candidate set generation and reducing computational expense by discarding non-viable attribute value combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional FIM algorithms are used to find all frequent itemsets, then complete segmentation is achieved, but computational complexity explodes due to the combinatorial nature of attribute combinations
Solution Approach 1:
The patent extracts and prioritizes only the most valuable segments based on value metrics rather than computing all possible itemsets. By applying value-based filtering early in the process, the algorithm extracts only the necessary information needed for high-value segmentation, avoiding the combinatorial explosion of traditional FIM approaches.
Solution Approach 2:
The patent performs preliminary filtering of candidate itemsets using value metrics before conducting full frequent itemset mining. By pre-identifying and prioritizing attribute combinations that are likely to be high-value based on preliminary metric evaluation, the system reduces the search space before applying computationally intensive FIM algorithms.
2Manufacturing precision
If all attribute combinations are evaluated to ensure distinctiveness, then segment quality improves, but processing time increases significantly
Solution Approach 1:
The patent replaces the mechanical approach of exhaustively comparing all segment pairs for distinctiveness with a metric-based filtering system. By using value metrics and similarity thresholds to pre-filter candidate segments, the system substitutes brute-force comparison with a more efficient metric-driven approach that maintains distinctiveness while reducing processing time.
3Loss of information
If value metrics are applied after segment generation, then high value segments can be identified, but the initial segmentation process remains computationally expensive
Solution Approach 1:
The patent applies value metrics in advance during candidate itemset generation rather than after complete segmentation. By evaluating value metrics during the candidate selection phase, the system identifies high-value segments earlier in the process, avoiding the need to compute and then filter all possible segments afterward.
Solution Approach 2:
The patent extracts and focuses computation on only those candidate itemsets that meet value metric thresholds. By removing low-value candidates early in the process through metric-based filtering, the system avoids spending computational resources on segments that would not contribute to the final high-value segment identification.
Data Source
AI summary
Systems and techniques for identifying segments in categorical data include receiving multiple transaction ID (TID) lists with univariate values that satisfy a thresholding metric with each TID list representing an occurrence of a single attribute in a set of transactions. The TID lists are stored with the univariate values that satisfy the thresholding metric in a data structure. In a loop, candidate itemsets to form from combinations of TID lists are determined using only the combinations of TID lists that satisfy categorical constraints. In the loop, for the candidate itemsets that satisfy categorical constraints, both the thresholding metric and a similarity metric are applied to the candidate itemsets. Final itemsets are formed from only the candidate itemsets that satisfy both the thresholding metric and the similarity metric.