find frequent itemsets A-Priori and PCY
Budget: $10 – $30 CAD
The main objective of this project is to find frequent itemsets by implementing two efficient
algorithms: A-Priori and PCY
You can choose your favorite programming language, preferably one of the following ones: C,
C++, Java, C#, or Python.
Experiments
• Perform the scalability study for finding frequent pairs of elements by dividing the dataset
into different chunks and measure the time performance. Provide the line chart. Provide
results for the following support thresholds: 1%, 5%, 10%. For example, if your chunk is
10% of the dataset, you have around 8,800 baskets. Therefore, if your support threshold is
5%, you should count the pairs that appear in at least 440 baskets
• Implement Multistage (3 Passes) version of PCY, using one extra hash table. (add the results
to the line chart)
• Implement Multihash version of PCY, using one extra hash table. (add the results to the line
chart)
algorithms: A-Priori and PCY
You can choose your favorite programming language, preferably one of the following ones: C,
C++, Java, C#, or Python.
Experiments
• Perform the scalability study for finding frequent pairs of elements by dividing the dataset
into different chunks and measure the time performance. Provide the line chart. Provide
results for the following support thresholds: 1%, 5%, 10%. For example, if your chunk is
10% of the dataset, you have around 8,800 baskets. Therefore, if your support threshold is
5%, you should count the pairs that appear in at least 440 baskets
• Implement Multistage (3 Passes) version of PCY, using one extra hash table. (add the results
to the line chart)
• Implement Multihash version of PCY, using one extra hash table. (add the results to the line
chart)