18474428. INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING APPARATUS simplified abstract (Fujitsu Limited)
Contents
INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING APPARATUS
Organization Name
Inventor(s)
Yuji Mizobuchi of Kawasaki (JP)
INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18474428 titled 'INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING APPARATUS
Simplified Explanation:
The patent application describes a computer system that analyzes multiple clustering results obtained from classifying sample programs into clusters based on their features and performance. The computer calculates evaluation values for each cluster in each result and selects the best clustering result based on these values.
- **Key Features and Innovation**:
- Clustering sample programs based on features and performance. - Calculating evaluation values for clusters in each result. - Selecting the best clustering result based on evaluation values.
Potential Applications: This technology can be applied in various fields such as data analysis, pattern recognition, and program optimization.
Problems Solved: The technology helps in efficiently organizing and analyzing large sets of sample programs for better decision-making and optimization.
Benefits: - Improved clustering accuracy. - Enhanced program reusability. - Efficient performance evaluation.
Commercial Applications: Title: "Optimized Program Clustering Technology for Enhanced Performance" This technology can be utilized in software development companies, data analysis firms, and research institutions to streamline program organization and improve overall performance.
Prior Art: Prior art related to this technology can be found in research papers on program clustering, data analysis, and optimization techniques.
Frequently Updated Research: Stay updated on advancements in program clustering algorithms, performance evaluation methods, and feature-based clustering techniques to enhance the application of this technology.
Questions about Program Clustering Technology: 1. What are the potential challenges in implementing this technology in real-world scenarios? 2. How does this technology compare to traditional clustering methods in terms of accuracy and efficiency?
Original Abstract Submitted
A computer acquires a plurality of clustering results, each of which differs in the number of clusters, by performing clustering that classifies a plurality of sample programs into two or more clusters based on features associated with description and an execution performance of each sample program. The computer calculates, for each of the two or more clusters in each of the clustering results, a first evaluation value based on an index value for reusability of sample programs included in the cluster and the execution performances of the sample programs. The computer calculates, for each of the clustering results, a second evaluation value based on two or more of the first evaluation values corresponding to the two or more clusters. The computer selects, based on the second evaluation values corresponding to the clustering results, one clustering result amongst the multiple clustering results.
