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TopBackground
The introduction of frequent itemsets (Agrawal et al., 1993), one of the first algorithms proposed for association rules mining was the AIS algorithm. The problem of association rules mining was introduced as well. This algorithm was improved later to obtain the Apriori algorithm (Agrawal et al., 1994).
Key Terms in this Chapter
Sequential Pattern Mining: Sequence mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence.
Breadth First Search: A graph search algorithm which tries all one-step extensions of current paths before trying larger extensions. This requires all current paths to be kept in memory simultaneously or at least their end points.
Neural Network: A neural network is a software (or hardware) simulation of a biological brain (sometimes called Artificial Neural Network or “ANN”). The purpose of a neural network is to learn to recognize patterns in your data. Once the neural network has been trained on samples of your data, it can make predictions by detecting similar patterns in future data.
Decision Tree: A schematic tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision or occurrence.
Transaction Records: It’s a database for storing transaction records. Every transaction record in the database has a unique identifier. A transaction record includes the order of all items involving the transaction.
Association Rule: Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a relational database or other information repository.
Data Warehouse: A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.