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Sequential Pattern Mining of Multimodal Data Streams in Dyadic Interactions Damian FrickerChen Yahoo Zhang Indiana University, Bloomington, IN 47408,U.S. Drinker, Huang, chenyu@indiana.eduAbstractInthispaperwe

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Collect the dataset containing sequential data.

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Define minimum support threshold for the patterns.

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Extract the frequent sequential patterns based on the minimum support threshold.

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Sequential pattern mining is the process of discovering patterns that occur in a sequence, such as transactions, events, or activities.

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The purpose of sequential pattern mining is to uncover hidden patterns, trends, or relationships in sequential data that can help in decision-making, forecasting, or process optimization.

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Information such as the sequence of events, the frequency of patterns, and the support or confidence levels of the patterns must be reported on sequential pattern mining.

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