Data Mining Pattern Shift Analysis via Temporal Comparison
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Solution Overview
Problem
Traditional data mining algorithms struggle to effectively analyze and compare shifts in data patterns over time and across different datasets or algorithms, leading to challenges in understanding and describing changes in data mining models.
Innovation Solution
A computer-implemented system that analyzes and compares data mining patterns across different datasets and algorithms, using a model component for storing data mining models and an analysis component to identify differences, with a machine learning and reasoning component for probabilistic and statistical analysis to infer changes and generate rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data mining algorithms are used to detect patterns in datasets, then data patterns can be identified, but the algorithms struggle to effectively analyze and compare shifts in data patterns over time and across different datasets or algorithms
Solution Approach 1:
The patent introduces a temporal dimension by comparing data patterns across multiple time points and datasets. The system analyzes pattern evolution by examining changes in pattern characteristics over time, adding a temporal axis to the traditional static pattern detection approach. This enables the detection of pattern shifts and transformations that would be invisible in single-time-point analysis.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing newly detected patterns against historical patterns and using the differences to refine pattern detection. The analysis component uses feedback from pattern comparison results to adjust and improve the detection of pattern shifts, creating a closed-loop system that enhances detection accuracy over time.
2Adaptability or versatility
If data mining models are updated over time to reflect changing datasets, then the models remain relevant, but it becomes difficult to describe and understand the changes in data patterns
Solution Approach 1:
The patent introduces an intermediary analysis component that acts as a bridge between the raw pattern data and the human interpreters. This component generates structured descriptions of pattern changes, translating complex numerical differences into interpretable format that shows what changed, where it changed, and how it changed. The intermediary preserves full pattern information while presenting it in an understandable manner.
Solution Approach 2:
The system performs preliminary analysis of pattern changes before final model updates are applied. By pre-analyzing and documenting pattern shifts before they are fully integrated into updated models, the system creates a record of changes that can be reviewed and understood. This preliminary documentation prevents information loss by capturing pattern evolution details before model transformation completes.
3Adaptability or versatility
If multiple algorithms are applied to the same data to find patterns, then more comprehensive pattern coverage is achieved, but comparing and validating the differences between algorithm outputs becomes complex
Solution Approach 1:
The patent segments the pattern comparison process into distinct analytical components. Instead of attempting to compare entire pattern sets at once, the system divides the comparison into element-level analyses, examining individual patterns and their characteristics separately. This segmentation breaks down the complex multi-algorithm comparison into manageable, systematic evaluations of pattern similarities and differences.
Solution Approach 2:
The analysis component is designed with universal functionality to handle comparisons across different algorithm outputs and data types. It implements a unified framework that can analyze patterns from any algorithm using consistent methods, making the comparison process independent of the specific algorithms being compared. This universal approach simplifies validation by providing a single standardized process for evaluating multiple algorithms.
Data Source
AI summary
Architecture for analyzing pattern shifts in data patterns of data mining models and outputting the results. This allows comparing and describing differences between two semantically similar sets of patterns (or mining models), and for analyzing historical changes in versions of the same model or differences in patterns found by two or more different algorithms applied to the same data. The architecture can also facilitate explaining data patterns that shift over time and over different data populations, and between versions of the same model that use different algorithms. A model component is employed for storing data mining models have respective sets of data patterns obtained from a dataset, and an analysis component analyzes the sets of the data patterns for difference data therebetween. The dataset can be a subsample of a larger set of data and can be analyzed by the analysis component over a time period.