(Information Science Guru) Machine Learning
- Represent the inclusion relationships of each pattern as a graph.
- Pattern: a set of elements.
- Example: In purchase data, a set of items bought by a customer.
- Pattern: a set of elements.
- Why use this representation?
- When you want to know the frequency or probability of each pattern (e.g., the frequency/probability of each item in purchase data).
- It takes times to perform exhaustive search.
- By using the Apriori algorithm,
- Since the inclusion relationships are known as a graph, there is no need to calculate the frequency/probability for all patterns.
- The frequency can be obtained by summing all the nodes below.
- The probability distribution can also be obtained using the Boltzmann machine method.
- It provides a probability distribution, not an empirical distribution.
- Empirical distribution: plotting the available data as is.
- Probability distribution: a distribution that has been adjusted to be closer to the true distribution based on the relationships between nodes. #Pattern Mining
- It provides a probability distribution, not an empirical distribution.