seoseanalüüsiga

Seoseanalüüsiga, also known as association rule mining, is a data mining technique used to discover interesting relationships between variables in large datasets. It aims to find rules that describe how frequently one item or set of items occurs in conjunction with another item or set of items. These rules are often expressed in the form of "if A then B," where A is the antecedent and B is the consequent.

The primary goal of seoseanalüüsiga is to identify patterns that can help in decision-making, such as product recommendations, market basket analysis, and fraud detection. For example, a common application is in retail, where analyzing customer purchase history might reveal that customers who buy bread also tend to buy milk. This association rule can then be used by retailers to strategically place these items together or offer bundled promotions.

Key metrics used in seoseanalüüsiga include support, confidence, and lift. Support measures the frequency of an itemset in the dataset. Confidence indicates the probability that the consequent B will occur given that the antecedent A has occurred. Lift measures how much more likely the consequent B is to occur when the antecedent A is present, compared to its baseline probability.

Algorithms like Apriori and FP-growth are commonly employed for seoseanalüüsiga. These algorithms efficiently identify frequent itemsets and generate association rules by pruning infrequent ones. The effectiveness of seoseanalüüsiga depends on the quality and size of the data, as well as the appropriate selection of thresholds for the metrics.