Knowledge Discovery using Unsupervised Cognition
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909468092006400 |
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| author | Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric |
| author_facet | Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric |
| contents | Knowledge discovery is key to understand and interpret a dataset, as well as to find the underlying relationships between its components. Unsupervised Cognition is a novel unsupervised learning algorithm that focus on modelling the learned data. This paper presents three techniques to perform knowledge discovery over an already trained Unsupervised Cognition model. Specifically, we present a technique for pattern mining, a technique for feature selection based on the previous pattern mining technique, and a technique for dimensionality reduction based on the previous feature selection technique. The final goal is to distinguish between relevant and irrelevant features and use them to build a model from which to extract meaningful patterns. We evaluated our proposals with empirical experiments and found that they overcome the state-of-the-art in knowledge discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Knowledge Discovery using Unsupervised Cognition Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric Machine Learning Artificial Intelligence Knowledge discovery is key to understand and interpret a dataset, as well as to find the underlying relationships between its components. Unsupervised Cognition is a novel unsupervised learning algorithm that focus on modelling the learned data. This paper presents three techniques to perform knowledge discovery over an already trained Unsupervised Cognition model. Specifically, we present a technique for pattern mining, a technique for feature selection based on the previous pattern mining technique, and a technique for dimensionality reduction based on the previous feature selection technique. The final goal is to distinguish between relevant and irrelevant features and use them to build a model from which to extract meaningful patterns. We evaluated our proposals with empirical experiments and found that they overcome the state-of-the-art in knowledge discovery. |
| title | Knowledge Discovery using Unsupervised Cognition |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.20064 |