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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  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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  1. Data Wrangling in Machine Learning
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  2. Importing Libraries and Dataset in Machine Learning
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  3. Handling Missing Data in Machine Learning
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  5. Encoding Categorical Data in Machine Learning
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  7. Splitting Dataset in Machine Learning
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  8. Splitting Dataset in Machine Learning - Practical
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  9. Normalizing the Data in Machine Learning - Part 1
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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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  17. Assignment : Data Preprocessing for Machine Learning
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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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  6. KNN in Machine Learning Practical - 1
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  8. Decision Trees for Classification in Machine Learning
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  10. Decision Tree Regressor in Machine Learning Practical - 1
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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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  3. K-Means Clustering in Machine Learning - Practical
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  4. Mall Customers Prediction using K Means Clustering in Machine Learning
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  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
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  6. Agglomerative Clustering in Machine Learning - Practical
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  10. Assignment : Unsupervised Learning Algorithms
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  1. Association Rule in Machine Learning - Apriori, FP Growth
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  2. Association Rule in Machine Learning - Apriori Practical
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  4. FP Growth in Machine Learning
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  5. Market Basket Analysis using FP Growth in Machine Learning
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  6. Assignment : Association Rule Mining
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  2. Upper Confidence Bound in Machine Learning - Practical
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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
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  1. Career Guideline
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