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MachineX: Total Support Tree for Association Rule Generation

In our previous blogs on Association Rule Learning, we have seen the FP-Tree and the FP-Growth algorithm. We also generated the frequent itemsets using FP-Growth. But a problem arises when we try to mine the association rules out of these frequent itemsets. Generally, the number of frequent itemsets is massive and to run an algorithm on them becomes very memory inefficient. So, to store these Continue Reading

MachineX: Understanding FP-Tree construction

In my previous blog, MachineX: Why no one uses apriori algorithm for association rule learning?, we discussed one of the first algorithms in association rule learning, apriori algorithm. Although even after being so simple and clear, it has some weaknesses as discussed in the above-mentioned blog. A significant improvement over the apriori algorithm is FP-Growth algorithm. To understand how FP-Growth algorithm helps in finding frequent Continue Reading

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