Get Evaluating Data Mining Models: A Pattern Language

The sequential pattern mining algorithm searches for frequent patterns in sequence databases. A sequence database consists of ordered elements or events. For example, a customer first buys bread, then eggs and cheese, and then milk. This forms a sequence consisting of three ordered events.

Based on data analysis usages: Frequent pattern mining often serves as an intermediate step for improved data understanding and more powerful data analysis. For example, it can be used as a feature extraction step for classification, which is often referred to as pattern-based classification.

Frequent patterns are itemsets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set, is a frequent itemset.

Some examples include market basket analysis, customer behavior analysis, web mining, bioinformatics, and network traffic analysis. Market basket analysis involves analyzing customer purchase patterns to identify connections between items and enhance sales strategies.

Descriptive patterns Associations: It shows the relationships between data and pre-defined association rules. ... Correlations: This is performed to find the statistical correlations between two data points to find if they have positive, negative, or no effect.

One of the most common ways to evaluate your data mining model is to split your data into two subsets: a training set and a testing set. The training set is used to build and train your data mining model, while the testing set is used to test and validate your data mining model.

Pattern Evaluation is defined as identifying strictly increasing patterns representing knowledge based on given measures. It find interestingness score of each pattern, and uses summarization and Visualization to make data understandable by user.

Pattern mining concentrates on identifying rules that describe specific patterns within the data. Market-basket analysis, which identifies items that typically occur together in purchase transactions, was one of the first applications of data mining.