• Source: Frequent pattern discovery

Frequent pattern discovery (or FP discovery, FP mining, or Frequent itemset mining) is part of knowledge discovery in databases, Massive Online Analysis, and data mining; it describes the task of finding the most frequent and relevant patterns in large datasets.
The concept was first introduced for mining transaction databases.
Frequent patterns are defined as subsets (itemsets, subsequences, or substructures) that appear in a data set with frequency no less than a user-specified or auto-determined threshold.




Techniques


Techniques for FP mining include:

market basket analysis
cross-marketing
catalog design
clustering
classification
recommendation systems
For the most part, FP discovery can be done using association rule learning with particular algorithms Eclat, FP-growth and the Apriori algorithm.
Other strategies include:

Frequent subtree mining
Structure mining
Sequential pattern mining
and respective specific techniques.
Implementations exist for various machine learning systems or modules like MLlib for Apache Spark.




References

Kata Kunci Pencarian:

• Penggalian data
• Frequent pattern discovery
• K-optimal pattern discovery
• Sequential pattern mining
• Data mining
• Association rule learning
• Overlapping circles grid
• Hair loss
• Age of Discovery
• Apriori algorithm
• Star Trek: Discovery season 5