Clustering Data Objects Using Sorted Attribute Values
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Solution Overview
Problem
Conventional clustering methods are inefficient and require multiple executions to optimize cluster definitions, consuming more processing power and memory, whereas existing unsupervised clustering methods lack an efficient algorithm for defining optimal clusters.
Innovation Solution
A computing system that sorts attribute values of data objects, calculates distance values, and defines clusters based on these values, using permutations of ranges to assign data objects, thereby optimizing cluster definitions in a single execution with reduced processing and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clustering methods are used to define clusters, then cluster definitions can be obtained, but multiple executions are required which increases processing power consumption and memory usage
Solution Approach 1:
The patent applies preliminary action by sorting attribute values and pre-calculating distance metrics before cluster assignment. This preprocessing step organizes data in advance, allowing the clustering algorithm to operate more efficiently in a single execution rather than requiring multiple runs to optimize cluster definitions.
Solution Approach 2:
The patent segments the clustering process into distinct phases: sorting attribute values, calculating distance metrics, determining cluster assignments, and optimizing cluster definitions. This segmentation allows each phase to be optimized independently, reducing overall processing requirements while maintaining cluster definition quality.
2Measurement precision
If conventional clustering methods are used to define clusters, then cluster definitions can be obtained, but multiple executions are required which increases time consumption
Solution Approach 1:
By performing preliminary sorting of attribute values and pre-calculation of distance metrics, the patent eliminates the need for multiple executions to optimize cluster definitions. This preliminary preparation enables the algorithm to achieve optimal cluster definitions in a single execution, significantly reducing time consumption.
Solution Approach 2:
The patent skips the iterative multiple-execution process by directly calculating cluster assignments based on pre-sorted data and distance metrics. This approach rushes through the clustering process in a single pass, avoiding the time-consuming repeated executions required by conventional methods.
3Productivity
If conventional clustering methods are used, then clusters can be formed, but processing efficiency is reduced due to multiple executions
Solution Approach 1:
The patent segments the clustering algorithm into modular steps: sorting attribute values, calculating distance metrics, determining cluster assignments, and optimizing definitions. This modular segmentation improves processing efficiency by allowing each step to be executed once in a structured sequence, rather than requiring multiple full-execution cycles of conventional methods.
Data Source
AI summary
Some embodiments provide a program that determines a plurality of data objects. Each data object in the plurality of data objects includes a first attribute and a second attribute. The program further sorts values of the first attribute of the plurality of data objects. The program also sorts values of the second attribute of the plurality of data objects. The program further determines a first distance value based on the sorted values of the first attribute of the plurality of data objects. The program also determines a second distance value based on the sorted values of the second attribute of the plurality of data objects. The program further defines a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value.