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The Flajolet-Martin algorithm is a probabilistic algorithm used in data science and big data contexts to estimate the number of distinct elements in a large data stream efficiently. Unlike traditional methods that require storing all elements, Flajolet-Martin leverages hashing and bit patterns to provide an approximate count, making it highly suitable for applications where memory and computational resources are constrained, such as network traffic analysis, database optimization, and streaming data analytics. The algorithm operates on the principle of hashing each element in the stream to a binary representation and observing the position of the least significant 1-bit in the hashed value. The key insight is that in a uniformly distributed hash, the probability of encountering a run of trailing zeros increases exponentially with the number of distinct elements. This allows the algorithm to infer the ___ _____ _________ _________ _______ _____.
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