MindMap Gallery Summary of methods for improving item set efficiency of FP-growth algorithm
Summary of FP-growth algorithm itemset efficiency improvement methods: FP tree: an effective way to encode data sets: the tree node gives a single element in the set and the number of occurrences in the sequence, and the path gives the occurrence of the sequence. frequency.
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Avatar 3 centers on the Sully family, showcasing the internal rift caused by the sacrifice of their eldest son, and their alliance with other tribes on Pandora against the external conflict of the Ashbringers, who adhere to the philosophy of fire and are allied with humans. It explores the grand themes of family, faith, and survival.
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Summary of methods for improving item set efficiency of FP-growth algorithm
introduction
FP-growth features
Discover frequent itemsets more efficiently
Cannot be used to discover association rules
Only two scans of the database are required
The basic process of discovering frequent sets
Build FP tree
Mining frequent itemsets from FP trees
FP trees: an efficient way to encode data sets
advantage
Generally faster than Apriori
shortcoming
It is difficult to implement
Performance degrades on some datasets
Applicable data types
Nominal type
FP
Frequent Pattern
FP tree
Connect similar elements through links
An element item can appear multiple times in an FP tree
The FP tree stores the frequency of occurrence of itemsets, and each itemset is stored in the tree in the form of a path.
Sets with similar elements share part of the tree
The tree will bifurcate only if the sets are completely different from each other
The tree node gives a single element in the set and the number of occurrences in the sequence, and the path gives the number of occurrences in the sequence.
node link
Links between similar items
Used to quickly discover the location of similar items
General process
Data collection
any method
Prepare data
Since a collection is stored, discrete data is required
If you want to process continuous data, you need to quantize it into discrete values
analyze data
any method
training algorithm
Build an FP tree and mine the tree
Test algorithm
none
Use algorithms
Can be used to identify frequently occurring elements for decision making, element recommendations, or predictions
Build FP tree
Create the data structure of FP tree
name
count
nodeLink
parent
Usually not required
It will be used when tracing back in subsequent chapters.
children
Build FP tree
In addition to the FP tree, a head pointer is required to point to the first instance of a given type
process
The first traversal of the data set obtains the frequency of occurrence of each element.
Remove elements that do not meet the minimum support and sort them
Build FP tree
Start from empty set
Read in each itemset
If the path does not exist, create it
If the path exists, increase the value of the existing element.
Mining frequent itemsets from an FP tree
The basic steps
Get conditional pattern basis from FP tree
Use the conditional pattern base to build a conditional FP tree
Iteratively repeat the first two steps until the tree contains an element item position
Extract conditional pattern base
conditional pattern base
A collection of paths ending with the element being searched for
Each path is a prefix path
That is, a prefix path is everything between the element you are looking for and the root node of the tree
Each prefix path is associated with a count value
Create conditional FP tree
For each frequent item, a conditional FP tree is created
process
Initially the tree has the empty set as the root node
Add the set that satisfies the minimum support in the conditional pattern base.
Mining the corresponding condition tree for the new set
Until the conditional tree has no elements
Example: Discover some co-occurring words in a Twitter feed
python-twitter library