sambandsaðferðir

Sambandsaðferðir, or association methods, are a class of algorithms used in data mining to discover interesting relationships between items in large datasets. These relationships are typically expressed in the form of association rules. The most well-known algorithm in this category is Apriori, which was introduced by Rakesh Agrawal, Tomasz Imieliński, and Arun Swami in 1993.

The primary goal of association methods is to find frequent itemsets, which are sets of items that appear together frequently in transactions. Once frequent itemsets are identified, association rules can be generated from them. An association rule is an implication of the form X -> Y, where X and Y are disjoint itemsets. For example, in a retail dataset, a rule like {bread, milk} -> {eggs} might suggest that customers who buy bread and milk are also likely to buy eggs.

Key measures used to evaluate the strength and interestingness of association rules include support, confidence, and lift. Support quantifies how frequently an itemset appears in the dataset. Confidence measures the conditional probability of Y occurring given that X has occurred. Lift indicates how much more likely Y is to occur given X, compared to its baseline probability.

Association methods are widely applied in various domains, including market basket analysis, recommendation systems, web usage mining, and bioinformatics. They help businesses understand customer purchasing patterns, optimize product placement, and personalize recommendations. While Apriori is a foundational algorithm, other methods like FP-growth have been developed to improve efficiency and handle larger datasets.