Clustering Using Random Variable Subsets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current clustering methods in data analysis often require all variables for accurate segmentation, which can be inefficient and may not account for correlations between variables, leading to suboptimal cluster determination.
Innovation Solution
The method involves randomly selecting subsets of variables and using clustering algorithms to determine the number of clusters, while decorrelating variables to reduce processing time and improve accuracy by preserving representative variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all variables are used for clustering, then clustering accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by using random subsets of variables instead of all variables for clustering. Multiple clustering operations are performed on different random subsets, and the results are aggregated to achieve accurate clustering without the computational cost of using all variables in each clustering operation.
Solution Approach 2:
The variable set is segmented into multiple random subsets for separate clustering operations. Each subset is processed independently through the clustering algorithm, and the results are combined to form the final clustering outcome, thereby distributing the computational workload.
2Measurement precision
If all variables are used for clustering, then clustering accuracy is improved, but device complexity increases
Solution Approach 1:
The method performs multiple clustering operations on partial variable subsets rather than one operation on all variables. This distributes the computational complexity across multiple simpler operations, reducing the complexity burden of each individual clustering operation while maintaining overall accuracy through aggregation.
3Measurement precision
If variable correlations are considered, then clustering accuracy is improved, but processing time increases
Solution Approach 1:
The patent handles variable correlations by performing multiple clustering operations on random subsets of variables. This approach implicitly accounts for correlations through the aggregation of results from multiple subsamples, achieving accurate clustering without the computational overhead of explicitly modeling correlations in a single operation.
Data Source
AI summary
A computing device to compute clusters using random subsets of variables is provided. Each data point of a plurality of data points is associated with a variable to define a plurality of variables. A subset of the plurality of variables is randomly selected. The subset does not include all of the plurality of variables. A number of clusters into which to segment the received data is determined. Cluster data that defines each cluster of the determined number of clusters is determined by executing a clustering algorithm with the received data using only the plurality of data points defined for each observation that are associated with the randomly selected subset of the plurality of variables. The determined cluster data is stored to cluster second data into the determined number of clusters. The second data is different from the received data.