| Autor: |
Vikram Bhushan, K. R. Suneetha, Sai Kishore |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Computer Networks and Inventive Communication Technologies ISBN: 9789811596469 |
| DOI: |
10.1007/978-981-15-9647-6_27 |
| Popis: |
This paper focuses on the different association rule mining algorithms and their applications in modern fields of deep learning and neural networks which form the pillar stones of new age problem solving. Association rule mining algorithms are categorized under “if–then” category as they have an antecedent (if) and a consequent (then). Deep learning is a machine learning technique which uses neural networks to pass the input to obtain outputs. The field of deep learning has become ubiquitous in all fields of problem solving due to its ability to accept raw inputs without feature extraction but this leads to overfitting. The algorithms Apriori and FPGrowth help in finding an association between features of the input which then can be reduced to a handful of features which then can be given to deep learning models. This paper attempts to explain these algorithms and their use in deep learning. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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