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258JOURNAL OF SOFTWARE, VOL. 9, NO. 1, JANUARY 2014Recent Frequent Item Mining Algorithm in a
Data Stream Based on Flexible Counter Windows
Banyan Go
School of Information Engineering, Hangzhou Polytechnic
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What is recent frequent item mining?
Recent frequent item mining is a data mining technique used to identify sets of items that frequently occur together in large datasets, helping to reveal patterns and relationships.
Who is required to file recent frequent item mining?
Entities that analyze large sets of transactional data to find commonly co-occurring items for market analysis, inventory management, or recommendation systems are typically required to implement recent frequent item mining.
How to fill out recent frequent item mining?
To fill out recent frequent item mining, one must collect relevant data, identify itemsets, use algorithms like Apriori or FP-Growth to discover frequent itemsets, and present the findings in a structured report.
What is the purpose of recent frequent item mining?
The purpose of recent frequent item mining is to enhance decision-making by identifying trends and associations in data, which can lead to improved marketing strategies and product placement.
What information must be reported on recent frequent item mining?
Information that must be reported includes the identified frequent itemsets, their support counts, confidence levels, and relevant statistics that provide insights into the data relationships.
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