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The associative model, often used in data mining and machine learning, has several advantages, including the ability to uncover hidden patterns and relationships in large datasets, which can enhance decision-making and predictive analytics. However, it also has disadvantages, such as potentially generating false associations or overfitting the data, leading to misleading conclusions. Additionally, the model can be computationally intensive and may require significant preprocessing of data to achieve meaningful results.
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What are the advantages and disadvantages of Associative model?
With the Associative model there is not record. When assembling all of the current information on a complex order the data storage needs to be re-visited multiple times. This could pose as a disadvantage. Some calculations seem to suggest that Associative database would need as many as four times the data reads as the relational database.
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