Sequential pattern mining [7] focuses on discovering frequent subsequences from an ordered sequence of events. Application of Gap-Constraints Given Sequential Frequent Pattern Mining for Protein Function Prediction Author links open overlay panel Hyeon Ah Park a Taewook Kim b Meijing Li a Ho Sun Shon c Jeong Seok Park d Keun Ho Ryu a These patterns are interesting as they represent some behavior common to several customers. Extracting information from logs is traditionally a data mining process [3]. Sequential pattern mining methods have been found to be applicable in a large number of domains. Applications for Pattern Discovery Using Sequential Data Mining. This algorithm used the historic data the maintenance of the available customer. Sequential pattern mining methods have been used to analyze this data and identify patterns. Due to the sequential nature of data streams, supporting SPM in streaming environments is commonly studied in the area of data stream mining as well. Another example of application of sequential pattern mining … Sequential data is omnipresent. Of course, this is a toy example. Sequential pattern mining (SPM) is a well‐studied theme in data mining, in which one aims to discover common sequences of item sets in a large corpus of temporal itemset data. However, searching for BGPs cannot be reduced to searching for frequent episodes in a TPF log. Sequential pattern mining can actually be applied on database containing hundreds of thousands of sequences. The use of sequential pattern mining in this recommendation system could help the doctor in increasing the recommendation, and helping the customer in taking the decision.

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