Data Clustering via Key Element Vector Extraction
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Solution Overview
Problem
Existing data clustering methods face challenges in effectively handling datasets with outliers and noise, which can lead to inaccurate grouping of data items and difficulty in identifying natural structures within the data.
Innovation Solution
A computer-implemented method and system for generating a key element vector that iteratively selects key elements from a dataset based on minimum average distance and maximum minimal distance criteria, using a similarity matrix to cluster data items into distinct groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering methods are used, then the clustering process can be performed, but the accuracy is reduced due to outliers and noise in the dataset
Solution Approach 1:
The patent extracts and removes outliers and noise from the dataset before performing clustering. The method identifies data items that do not conform to the natural structures in the dataset and separates them, allowing the remaining clean data to be clustered with higher accuracy.
Solution Approach 2:
The patent performs preliminary data cleaning and preprocessing steps before the actual clustering process. By preparing the dataset in advance by removing harmful elements like outliers and noise, the subsequent clustering operation achieves better results without being affected by these distortions.
2Measurement precision
If manual sorting is used to achieve accurate clustering, then clustering accuracy can be improved, but labor and time requirements increase significantly
Solution Approach 1:
The patent implements an automated clustering system that performs data analysis, outlier detection, and cluster formation without human intervention. The algorithm independently identifies natural structures in the data and groups items accordingly, eliminating the need for manual sorting while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual sorting with an automated computational algorithm. The system uses distance calculations, similarity measurements, and clustering algorithms to perform what would otherwise require human judgment and physical sorting actions, dramatically improving productivity while maintaining accuracy.
3Productivity
If automated clustering is used to improve productivity, then labor and time are reduced, but accuracy may deteriorate due to inability to handle outliers and noise
Solution Approach 1:
The patent incorporates feedback mechanisms where the clustering algorithm evaluates the quality of clusters formed and adjusts its parameters accordingly. The system measures distances between data items, assesses cluster coherence, and refines the clustering process to achieve both high productivity and accuracy by automatically adapting to the data characteristics.
Solution Approach 2:
The patent dynamically adjusts clustering parameters such as distance thresholds, number of clusters, and similarity criteria based on the characteristics of the input data. By changing these parameters automatically according to the dataset's properties, the system maintains high accuracy across different data types while preserving the efficiency benefits of automation.
Data Source
AI summary
A system for clustering data comprises a database for storing a plurality of data items, a clustering unit comprising components operable to receive and cluster the plurality of data items, and output clustered data items. A method of clustering data comprises receiving a plurality of data items from a database; computing distances between each pair of data items in the plurality of data items; until a stopping criterion is reached, identifying key elements, merging data items not identified as key elements with the nearest key elements, updating the computed distances; once the stopping criterion is reached, outputting cluster identities of the plurality of data items.