Clustering Method Using Exact Matching Data Sets
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Solution Overview
Problem
Existing clustering methods, such as the K-means algorithm, face challenges in accurately determining initial clustering centers, which can lead to suboptimal clustering results due to the random selection of centers, affecting both speed and accuracy.
Innovation Solution
A method that acquires data with exact matching information, determines data sets with the same information, calculates a clustering center based on the amount of data in each set, and clusters data using these centers to improve the accuracy and reasonableness of the clustering process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random selection of clustering centers is used, then the clustering process is simple and fast, but the accuracy and reliability of clustering results deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-processing the data to identify and remove duplicate entries before determining clustering centers. This preliminary data cleaning ensures that the centers are selected from high-quality, unique data points, thereby improving the reliability of clustering results while maintaining computational efficiency. The duplicate removal is performed beforehand to prevent poor center selection that would compromise accuracy.
2Loss of time
If duplicate data is not removed, then the data processing is faster, but the clustering center determination becomes inaccurate
Solution Approach 1:
The patent implements preliminary action by performing duplicate removal as a pre-step before clustering center determination. The system identifies and eliminates duplicate data entries in advance, ensuring that subsequent center selection is based on unique, high-quality data. This preliminary processing, while adding some time cost, significantly improves the precision of clustering centers and overall clustering accuracy.
Solution Approach 2:
The patent applies the extraction principle by selectively removing duplicate data entries from the dataset before clustering center determination. This extraction of problematic duplicate records ensures that only unique, representative data points are considered when selecting clustering centers, thereby improving measurement precision and the accuracy of clustering results.
3Device complexity
If clustering centers are selected without considering data distribution, then the algorithm is simpler, but the clustering results become suboptimal
Solution Approach 1:
The patent applies preliminary action by pre-processing the data distribution through duplicate removal and data cleaning before selecting clustering centers. This preliminary preparation ensures that the data reflects true underlying distributions without distortion from duplicates, enabling simpler algorithms to produce higher-quality clustering results. The pre-processing step prepares the data in advance, eliminating the need for complex center selection procedures while improving result quality.
Data Source
AI summary
A clustering method includes: multiple data to be clustered are acquired, the data to be clustered including exact matching information; multiple data sets consisting of data to be clustered with same exact matching information are determined; a clustering center is determined according to the amount of data to be clustered in each of the multiple data sets; and the multiple data to be clustered are clustered according to the clustering center to obtain target clusters. Accordingly, multiple data sets each consisting of data to be clustered with same exact matching information are determined, and a clustering center is determined according to the amount of the data to be clustered in each of the multiple data sets, so that unreasonable determination of the clustering centers can be improved, and accuracy of a clustering algorithm can further be improved.