CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques
Mining technologies depend upon their outcomes, focusing only on certain data features within the database. They select only certain features related to the process from diverse integrated data resources and transform them into a form suitable for mining tasks. Different implementations of mining te...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/59/1/14 |
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| Summary: | Mining technologies depend upon their outcomes, focusing only on certain data features within the database. They select only certain features related to the process from diverse integrated data resources and transform them into a form suitable for mining tasks. Different implementations of mining techniques run on data sources, which may be of considerable volume, to extract different knowledge outcomes suitable for various analyses and decision-making processes. The proposed study provides the design and development of the Clustering of Non-Associated Items set (CNAIS) within a transactional database. The development of the algorithm and its application to the data set are described and the results are noted. Comparisons with state-of-the-art methods show that CNAIS exhibits better performance. |
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| ISSN: | 2673-4591 |