chap7_extended_association_analysis.pptVIP

(C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 Data MiningAssociation Rules: Advanced Concepts and Algorithms Continuous and Categorical Attributes Handling Categorical Attributes Transform categorical attribute into asymmetric binary variables Introduce a new “item” for each distinct attribute-value pair Example: replace Browser Type attribute with Browser Type = Internet Explorer Browser Type = Mozilla Browser Type = Mozilla Handling Categorical Attributes Potential Issues What if attribute has many possible values Example: attribute country has more than 200 possible values Many of the attribute values may have very low support Potential solution: Aggregate the low-support attribute values What if distribution of attribute values is highly skewed Example: 95% of the visitors have Buy = No Most of the items will be associated with (Buy=No) item Potential solution: drop the highly frequent items Handling Continuous Attributes Different kinds of rules: Age?[21,35) ? Salary?[70k,120k) ? Buy Salary?[70k,120k) ? Buy ? Age: ?=28, ?=4 Different methods: Discretization-based Statistics-based Non-discretization based minApriori Handling Continuous Attributes Use discretization Unsupervised: Equal-width binning Equal-depth binning Clustering Supervised: Discretization Issues Size of the discretized intervals affect support confidence If intervals too small may not have enough support If intervals too large may not have enough confidence Potential solution: use all possible intervals Discretization Issues Execution time If intervals contain n values, there are on average O(n2) possible ranges Too many rules Approach by Srikant Agrawal Preprocess the data Discretize attribute using equi-depth partitioning Use partial completeness measure to determine number of partitions Merge adjacent intervals as long as support is less than max-support Apply existing association rule min