What are the different pattern evaluation measures in data mining?

Question

在数据挖掘中，使用不同的算法（例如Apriori算法，FP-Tree等）发现频繁项集。那么这些模式评估方法吗？

Answer 1

You can try Association Rules (apriori for example), Collaborative Filtering (item-based or user-based) or even Clustering.

I don't know what you are trying to do, but if you have a data-set and you need to find the most frequent item-set you should try some of the above techniques.

If you're using R you should explore the arules package for association rules (for example).

Answer 2

Apriori algorithm and FP-tree algorithm is used to find frequent itemsets for the given transactional data. This would help in market basket analysis applications. For pattern evaluation, there are many components namely:

1. support,
2. confidence,
3. Lift,
4. Imbalance ratio, etc.

More details can be seen at the paper:

Selecting the right interestingness measure for association patterns by Pang Ning Tan, Vipin Kumar, Jaideep Srivastava, KDD 2002.

• Refer URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.1494&rep=rep1&type=pdf