Complete Machine Learning Course > Association Rule Mining

FP Growth in Machine Learning

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The Apriori Algorithm is commonly used for frequent pattern mining, and FP-growth is an upgraded version of it (AKA Association Rule Mining). It's an analytical technique for identifying common patterns or correlations in data sets.

The first strategy is Mine Merge, which does not require a specific mining algorithm and can be applied to FP-growth without any changes. The second is an FP-growth implementation of the general concept of Common Counting.

  • Compared to Apriori, which searches the transactions for each iteration, this method only needs to scan the database twice.
  • This approach does not do item pairing, which makes it faster.
  • The database is kept in memory in a compressed form.

If the number of unique elements in the collection is n, the time complexity, in my opinion, should be O(n2). The depth of the tree determines the complexity of the search for pathways in the FP tree for each entry of the header table.

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Shafi Akhtar
5

None

A
Aniket Kumar prasad
5

Very helpful and easy to understand all the concepts, best teacher for learning ML.

R
Rishu Shrivastav
5

explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?

V
VIKAS CHOUBEY
5

very nicely explained

V
Vrushali Kandesar
5

Awesome and very nicely explained!!! One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)

M
Mohd Mushraf
5

Amazing Teaching

J
Juboraj Juboraj
5

Easy to understand & explain details.

J
Joydeb
5

Awesome Course sir and your teaching style is very GOOD.

S
Shaga Chandrakanth Goud
5

Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld

N
Neel Khairnar
5

Kushal is very good explainer he is covering all topics nicely 👍

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  1. Distance Metrics in Machine Learning
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  2. K-Means Clustering in Machine Learning
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    9m 49s
  4. Mall Customers Prediction using K Means Clustering in Machine Learning
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    4m 39s
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  1. Association Rule in Machine Learning - Apriori, FP Growth
    7m 19s
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    7m 15s
  3. Market Basket Analysis using Apriori in Machine Learning
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  4. FP Growth in Machine Learning
    7m 43s
  5. Market Basket Analysis using FP Growth in Machine Learning
    11m 46s
  6. Assignment : Association Rule Mining
    46s

  1. Reinforcement Learning in Machine Learning - Multi Armed Bandits
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