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over time. Potential scalability issues can be addressed by breaking the data
into more manageable subsets, so long as memory generated from previous pre-
sentations is fed back into the TEA prior to new data presentation. Using this
approach the algorithm can learn through association from past experiences to
maintain a high success rate in detecting and recording prevalent trends.
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