'Association rule mining task' presentation slideshows

DATA MINING LECTURE 3

DATA MINING LECTURE 3. Frequent Itemsets Association Rules. Frequent Itemsets. Given a set of transactions, find combinations of items ( itemsets ) that occur frequently. : support, number of transactions that contain I minsup. Market-Basket transactions.

1.88k views • 158 slides

Data Mining Toon Calders

Data Mining Toon Calders. Why Data mining?. Explosive Growth of Data: from terabytes to petabytes Data collection and data availability Major sources of abundant data. Why Data mining?. We are drowning in data, but starving for knowledge!

1.09k views • 94 slides

Frequent Item Mining

Frequent Item Mining. What is data mining?. =Pattern Mining? What patterns? Why are they useful? . Definition: Frequent Itemset. Itemset A collection of one or more items Example: {Milk, Bread, Diaper} k-itemset An itemset that contains k items Support count ( ?)

2.19k views • 74 slides

Association Analysis: Basic Concepts and Algorithms

Association Analysis: Basic Concepts and Algorithms. Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction. Association Rule Mining. Example of Association Rules. Market-Basket transactions.

930 views • 70 slides

AMCS/CS 340: Data Mining

1.03k views • 61 slides

Introduction to Data Mining Mining Association Rules

Introduction to Data Mining Mining Association Rules. Reference: Tan et al: Introduction to data mining. Some slides are adopted from Tan et al. What is Data Mining?.

446 views • 42 slides

Association Rule Mining

Association Rule Mining. The Task. Two ways of defining the task General Input: A collection of instances Output: rules to predict the values of any attribute(s) (not just the class attribute) from values of other attributes E.g. if temperature = cool then humidity =normal

534 views • 20 slides