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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S
Suresh Kumar
5

Hi Sir, I want a clearity up on these 1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.

A
Ayush Bharti
4

how can i download the finaldata.csv?

J
Jagannath Mahato
5

Hello Kushal Sir! Your way of teaching is very good. I thank you from my heart ❤️ that you are providing such good content for free.

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Muhammad Qasim
5

Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world. Extremely helpful and I recommend to my peer as well for this course.

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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.

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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

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Course Content

  1. Getting Started with Machine Learning
    4m 4s

  1. Introduction to Machine Learning
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  2. Environment Setup Part 1
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  3. Environment Setup Part 2
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  4. Environment Setup Part 3
    2m 34s

  1. Data Wrangling in Machine Learning
    12m 26s
  2. Importing Libraries and Dataset in Machine Learning
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  3. Handling Missing Data in Machine Learning
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  4. Handling Missing Data in Machine Learning - Practical
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  5. Encoding Categorical Data in Machine Learning
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  6. Encoding Categorical Data in Machine Learning - Practical
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  7. Splitting Dataset in Machine Learning
    3m 38s
  8. Splitting Dataset in Machine Learning - Practical
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  9. Normalizing the Data in Machine Learning - Part 1
    6m 19s
  10. Normalizing the Data in Machine Learning - Part 2
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  11. Finding Machine Learning Datasets
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  12. Exploratory Data Analysis in Machine Learning
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  13. Plotting Graphs in Machine Learning - Part 1
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  15. Distribution Models in Machine Learning - Part 1
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  16. Distribution Models in Machine Learning - Part 2
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  17. Assignment : Data Preprocessing for Machine Learning
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  1. Machine Learning Paradigms
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  1. Overview of Classification
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  2. Logistic Regression in Machine Learning
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  3. Logistic Regression in Machine Learning - Practical - 1
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  5. KNN in Machine Learning
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  6. KNN in Machine Learning Practical - 1
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  7. KNN in Machine Learning - Practical 2
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  8. Decision Trees for Classification in Machine Learning
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  9. Decision Trees for Classification in Machine Learning - Practical - 1
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  10. Decision Trees for Classification in Machine Learning - Practical - 2
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  11. Decision Tree Iterative Dichotomiser in Machine Learning - 3
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  12. Random Forest in Machine Learning
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  13. Support Vector Machine Classifier
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  17. Naive Bayes Classifier in Machine Learning Practical - 1
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  18. Naive Bayes Classifier in Machine Learning Practical - 2
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  19. Evaluating Classification Models Performance in Machine Learning
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  20. Evaluating Classification Models Performance in Machine Learning Practical
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  21. Assignment : Supervised Learning Algorithms
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  3. Salary Prediction using Linear Regression in Machine Learning
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  4. Multi-Linear Regression in Machine Learning
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  5. Startup Prediction using Multiple Regression in Machine Learning
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  6. Support Vector Regressor in Machine Learning
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  8. Support Vector Regressor in Machine Learning Practical - 2
    15m 46s
  9. Decision Tree Regressor in Machine Learning
    4m 53s
  10. Decision Tree Regressor in Machine Learning Practical - 1
    5m 2s
  11. Decision Tree Regressor in Machine Learning Practical - 2
    17m 10s
  12. Regressor Model Selection in Machine Learning
    9m 7s
  13. Evaluating Regression Model Performance in Machine Learning
    8m 46s
  14. Evaluating Regression Model Performance in Machine Learning - Practical
    11m 35s
  15. Assignment : Regression Algorithms
    1m 12s

  1. Distance Metrics in Machine Learning
    6m 10s
  2. K-Means Clustering in Machine Learning
    11m 3s
  3. K-Means Clustering in Machine Learning - Practical
    9m 49s
  4. Mall Customers Prediction using K Means Clustering in Machine Learning
    18m 3s
  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
    4m 39s
  6. Agglomerative Clustering in Machine Learning - Practical
    4m 45s
  7. Divisive Clustering in Machine Learning - Practical
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  8. DBscan Spatial Clustering in Machine Learning
    8m 50s
  9. Mall Customers Prediction using Hierarchical Clustering in Machine Learning
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  10. Assignment : Unsupervised Learning Algorithms
    2m 41s

  1. Association Rule in Machine Learning - Apriori, FP Growth
    7m 19s
  2. Association Rule in Machine Learning - Apriori Practical
    7m 15s
  3. Market Basket Analysis using Apriori in Machine Learning
    17m 50s
  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
    21m 4s
  2. Upper Confidence Bound in Machine Learning - Practical
    8m 41s
  3. Thompson Sampling in Machine Learning - Practical
    4m 18s
  4. Q Learning in Machine Learning
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  5. Assignment : Reinforcement Learning
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  1. Overview of Dimensionality Reduction in Machine Learning
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  2. Principal Component Analysis in Machine Learning
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  3. Principal Component Analysis in Machine Learning - Practical
    6m 35s
  4. Linear Discriminant Analysis in Machine Learning
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  5. Linear Discriminant Analysis in Machine Learning - Practical
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  6. Assignment : Dimensionality Reduction
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  1. Introduction to Keras and Deep Learning
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  1. Reinforcement Learning Project - Teach a Taxi Part 1
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  1. Course Summary
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  1. Interview Questions Part 1
    19m 22s
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    12m 23s
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  1. Career Guideline
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