Cluster Classification Visualization for Interpreting Similar Data
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Solution Overview
Problem
Existing clustering apparatuses struggle to assist users in interpreting clustering results, particularly when input data is complex and similar data within different clusters are indistinguishable, requiring specialized knowledge.
Innovation Solution
A clustering apparatus that calculates a feature vector from input data, divides it into clusters, selects specific clusters, extracts target data, calculates a degree of cluster classification, and converts the data to emphasize differences between clusters, using methods like gradient ascent or descent to enhance pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clustering is performed on complex input data, then data can be divided into clusters, but it becomes difficult to interpret the clustering result and distinguish differences between similar data in different clusters
Solution Approach 1:
The patent introduces an intermediary visualization system that translates complex clustering results into intuitive visual representations. The system generates visual data representing cluster characteristics and displays them in a comprehensible format, acting as a mediator between the complex clustering algorithm and the user's interpretation needs.
Solution Approach 2:
The patent transforms the clustering results by changing parameters into visual characteristics. It extracts key parameters from clustered data and converts them into visual attributes such as position, size, color, or shape in the visualization, making abstract clustering concepts tangible and interpretable.
2Measurement precision
If specialized knowledge is used to find differences between clusters, then accurate interpretation is possible, but common users cannot interpret the clustering result
Solution Approach 1:
The patent enables the system to automatically generate and present interpretable visual representations of clustering results without requiring user expertise. The visualization system self-services by automatically extracting meaningful patterns and presenting them in an accessible format that any user can understand.
Solution Approach 2:
The patent uses visual attributes including color changes to represent different clusters and their characteristics. By assigning distinct visual properties to different clusters and highlighting differences through color variations, the system makes cluster distinctions immediately apparent to users regardless of their specialized knowledge.
3Ease of operation
If visualization technology for class classification is applied to clustering, then class likeness can be visualized, but it cannot assist interpretation of clustering results when original data is similar
Solution Approach 1:
The patent moves clustering interpretation from the original data dimension to a visual dimension. By projecting clustering characteristics into a separate visual space with different attributes (position, size, color), it creates an additional dimension for understanding that preserves discriminative information even when original data appears similar.
Solution Approach 2:
The patent segments the visualization into distinct components representing different cluster characteristics. It separates and independently visualizes various attributes of clusters, allowing users to examine each aspect individually and understand the differences between clusters that may not be apparent in the original data.
Data Source
AI summary
According to one embodiment, a clustering apparatus includes processing circuitry. The processing circuitry is configured to: acquire input data; calculate a feature vector from the input data; divide the input data into two or more clusters based on the feature vector; select a first cluster from the two or more clusters; select a second cluster different from the first cluster from the two or more clusters; extract target data from the input data; calculate a degree of cluster classification indicating an index by which the target data is classified into the first cluster or the second cluster based on the target data, the first cluster, and the second cluster; and convert the target data based on the degree of cluster classification.