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Explain Multidimensional and multilevel association mining with example.

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Association mining, a data mining technique, helps identify interesting relationships or associations between items in a dataset. Multidimensional association mining and multilevel association mining are advanced variations of this technique 

1. Multidimensional Association Mining:

Multidimensional association mining focuses on discovering associations involving multiple attributes or dimensions, rather than just individual items. It's especially useful when dealing with datasets that have structured attributes or dimensions. A classic example of multidimensional association mining is market basket analysis with attributes such as product categories, customer demographics, and time periods.

Example:

Consider a retail store's sales data with multiple dimensions: products, customer segments, and time periods. You want to discover associations between these dimensions to optimize sales strategies. You might discover that during the holiday season (time dimension), customers in the "young adults" segment tend to buy more "electronic gadgets" (product dimension) and "apparel" together. This insight can inform targeted marketing campaigns during the holidays for this specific customer segment.

2. Multilevel Association Mining:

Multilevel association mining is concerned with identifying associations at different levels of granularity within a dataset. It can help uncover associations at various levels of abstraction, which can be useful in hierarchical data structures or when dealing with data hierarchies.

Example:

Let's say you have a supermarket's transaction data, and the products are organized hierarchically into categories and subcategories. You want to perform multilevel association mining to uncover associations at different levels of product granularity.

  • Level 1 (High-Level): You may discover that "Groceries" and "Personal Care" are frequently purchased together.
  • Level 2 (Medium-Level): Digging deeper, you might find associations like "Cereals" and "Milk" within the "Groceries" category.
  • Level 3 (Low-Level): Finally, at the lowest level of granularity, you identify specific associations like "Brand X Cereal" and "Brand Y Milk" being frequently purchased together.

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