Classification Device Using Commonality Value for Data Grouping
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Solution Overview
Problem
Conventional methods for classifying discrete data into groups struggle to achieve optimal classification that aligns with the analyst's purpose, as they rely solely on occurrence probability and the number of groups, failing to consider other critical factors such as commonality and the analyst's specific objectives.
Innovation Solution
A classification method that reduces the commonality value of variable values among groups by calculating a commonality value and adjusting the classification based on it, alongside the occurrence probability, to create groups that better serve the analyst's objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If classification is based solely on occurrence probability and number of groups, then the classification process is simple, but the classification accuracy and alignment with analyst's purpose deteriorates
Solution Approach 1:
The patent introduces a new parameter (commonality value) to the classification process, changing the evaluation criteria from solely occurrence probability to a combination of occurrence probability and commonality value. This allows the system to achieve better classification accuracy by considering both how frequently items occur together and how uniquely they characterize specific groups, resolving the contradiction between simplicity and accuracy.
2Reliability
If classification minimizes commonality among groups, then group distinctiveness improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent uses the commonality value as an intermediary metric that bridges the gap between group composition and group distinctiveness. By calculating commonality values based on item co-occurrence patterns across groups, the system provides a measurable quantity that reflects how well groups are differentiated, making the abstract concept of group distinctiveness detectable and optimizable.
Data Source
AI summary
A classification method executed by a computer for classifying a plurality of records into a plurality of groups, the classification method includes: acquiring the plurality of records, the plurality of records including a variable value respectively; tentatively classifying the plurality of records into the plurality of groups; calculating a commonality value indicating a degree of commonality of the variable value among the plurality of groups, based on the variable value included in each of the tentatively classified groups; classifying the plurality of records into the plurality of groups based on the commonality value; and outputting a result of the classifying.