• Bookmarks

    Bookmarks

  • Concepts

    Concepts

  • Activity

    Activity

  • Courses

    Courses


Market basket analysis is a data mining technique used to discover associations between items purchased together, helping businesses understand consumer behavior and optimize marketing strategies. It is commonly applied in retail to increase sales through targeted promotions and product placements by identifying frequent itemsets and association rules.
Concept
Concept
Concept
Lift is the aerodynamic force that acts perpendicular to the relative wind and supports the weight of an aircraft in flight. It is generated primarily by the wings and is a result of pressure differences created by the airfoil shape and angle of attack as the aircraft moves through the air.
A frequent itemset refers to a collection of items that appear together in a dataset with a frequency that meets or exceeds a predefined threshold. This concept is fundamental in data mining for identifying patterns, associations, and correlations within large datasets, enabling insights into consumer behavior, inventory management, and more.
Rule generation is the process of creating a set of guidelines or algorithms that dictate how decisions are made or actions are taken within a system. It involves identifying patterns, establishing criteria, and formulating rules that can be applied consistently to achieve desired outcomes.
1
Data mining is the process of discovering patterns and insights from large datasets by using machine learning, statistics, and database systems. It enables organizations to transform raw data into meaningful information, aiding in decision-making and predictive analysis.
Pattern recognition is the process of identifying and categorizing data based on its underlying structure or regularities, often using machine learning algorithms. It is fundamental in fields such as computer vision, speech recognition, and bioinformatics, where it enables the automation of complex tasks by learning from examples.
Frequent Pattern Mining is a data mining technique used to identify recurring patterns, associations, or structures among sets of items in large databases, which is crucial for tasks like market basket analysis and recommendation systems. It involves discovering itemsets that appear frequently together, allowing businesses to understand customer behavior and optimize their strategies accordingly.
Knowledge discovery is the process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. It involves several steps including data preparation, data mining, and interpretation of results to extract meaningful information that can inform decision-making.
Sequence mining is a data mining technique focused on identifying and analyzing patterns or regularities in sequential data, which is crucial in various fields like bioinformatics, market basket analysis, and web usage mining. It involves discovering frequent subsequences, patterns, or trends over time, enabling predictive analytics and decision-making based on historical data sequences.
Spatial association rules are a data mining technique used to discover interesting relationships between spatial and non-spatial attributes in large datasets. They are particularly useful in geographic information systems (GIS) for identifying patterns and correlations in spatial data, such as environmental, urban, and agricultural datasets.
Base association refers to the fundamental relationships or connections between basic elements or units within a system, often serving as the underlying framework for more complex interactions. Understanding these associations is crucial for analyzing and predicting the behavior of the entire system or network.
Association refers to the relationship or correlation between two or more variables, indicating how changes in one variable might relate to changes in another. It is a fundamental concept in statistics and data analysis, helping to identify patterns, predict outcomes, and inform decision-making processes.
Itemset mining is like finding groups of toys that you often play with together. It's a way to discover patterns in a big box of toys, like always playing with a teddy bear and a toy car at the same time.
Relational Pattern Discovery involves identifying structured relationships or patterns within complex datasets, which can reveal insights about the interactions between different entities. This process is crucial in fields like bioinformatics, natural language processing, and social network analysis, where understanding these relationships can lead to more informed decision-making and innovation.
Co-occurrence refers to the frequency or relative frequency with which two or more items appear together in a given context, often used in the fields of data mining, linguistics, and information retrieval to identify associations and patterns. Recognizing co-occurrence patterns can enhance understanding of the relationships between entities, aiding in tasks such as recommendation systems, natural language processing, and market basket analysis.
3