Data Mining Rule Prioritization via Monte-Carlo Simulation
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Solution Overview
Problem
Current data mining techniques are cumbersome and time-consuming, particularly when dealing with large datasets, as they fail to efficiently extract statistically significant patterns and prioritize insights, and do not effectively account for attribute types such as demographics or transaction data, leading to inefficiencies in decision-making processes.
Innovation Solution
A system and method that uses Monte-Carlo simulation to generate scores for support, confidence, and lift metrics to prioritize and modify rules, enabling the extraction of statistically significant patterns and providing recommendations for decision-making by automatically prioritizing rules and transforming undesired data segments into desired ones through generalization, specialization, and what-if scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data mining techniques are used to analyze large datasets, then patterns can be identified, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple candidate rules with associated metrics (support, confidence, lift) before user analysis. This pre-computation of statistical measures and rule generation reduces the time users would otherwise spend on manual pattern exploration, while maintaining comprehensive pattern identification coverage.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems. The data mining engine automatically generates rules, calculates statistical metrics, and prioritizes patterns using algorithms rather than human analysts manually examining data, significantly reducing analysis time while preserving pattern identification accuracy.
2Loss of information
If comprehensive data analysis is performed on all data segments, then all patterns are captured, but the complexity and time required increases significantly
Solution Approach 1:
The system changes parameters by introducing multiple quantitative metrics (support threshold, confidence threshold, lift threshold) to characterize and filter patterns. By varying these parameters, the system can adjust the comprehensiveness of pattern capture versus the complexity of analysis, allowing users to balance between capturing all patterns and managing system complexity through parameter optimization.
3Ease of operation
If manual rule modification and analysis is performed, then user control is maintained, but the process requires significant user time, effort and skill
Solution Approach 1:
The system enables self-service by automatically generating, scoring, and prioritizing rules with computed metrics (support, confidence, lift). Users can review and modify rules based on these pre-computed indicators without needing to perform complex manual analysis, reducing the time, effort, and skill required while maintaining user control over rule refinement and selection.
4Reliability
If traditional rule generation methods are used, then rules are generated, but they are not prioritized or validated for statistical significance
Solution Approach 1:
The system implements feedback by computing statistical metrics (support, confidence, lift) for each generated rule and using these metrics to prioritize and validate rules. This feedback loop ensures that only statistically significant rules are presented to users, improving reliability while the automated prioritization based on these metrics enhances productivity by filtering out insignificant patterns.
Data Source
AI summary
A method and system for identifying and prioritizing business useful insights from hidden patterns. This invention relates to data mining techniques and more particularly to identify and prioritize insights from a plurality of insights present in a large set of data. Insight exploration is a method and system that enables the user to generate actionable insights, prioritize them for a given data. This falls broadly within the field of data mining. The primary achievement of this invention is to take a rule in if-then format and then systematically process them to identify actionable information from them. In that process, the system automatically prioritizes the rules, generates other rules and analyzes the path that leads to desired behavioral changes.