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Application of commonly used pattern mining algorithms in machine learning
Application of commonly used pattern mining algorithms in machine learning
Pattern mining algorithm is a data mining technique used to identify patterns and relationships in data sets. These algorithms can be applied for a variety of purposes, such as discovering associations between different variables. Pattern mining algorithms typically analyze large data sets to find repeating patterns or associations between variables. Once these patterns are identified, they can be used to predict future trends or results, or to discover potential relationships in the data. Pattern mining algorithms have wide applications in many fields, including marketing, finance, healthcare, etc. By using these algorithms, we can help us better understand the data and obtain valuable information from it.
Common pattern mining algorithms
Apriori algorithm: an algorithm for finding frequent itemsets in transactional databases, it is efficient and It is widely used in association rule mining tasks.
Recurrent Neural Network (RNN): A type of neural network designed to process sequential data due to their ability to capture temporal dependencies in the data.
Long short-term memory (LSTM) is a type of recurrent neural network specifically designed to better retain information in long-term memory. It can effectively capture long-term dependencies in data and is therefore widely used in tasks such as language translation and language generation.
Sequential Pattern Discovery Using Equivalence Classes (SPADE): A method of finding frequent patterns in sequential data by grouping together items that are equivalent in some sense. This approach is capable of handling large data sets and is relatively efficient, but is not suitable for sparse data.
PrefixSpan algorithm: An algorithm that finds frequent patterns in sequential data by building a prefix tree and pruning infrequent items. PrefixScan is capable of handling large data sets and is relatively efficient, but it is not suitable for sparse data.
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