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MULTILEVEL ASSOCIATION RULES:
- Association rules generated from mining data at multiple levels of abstraction are called multiple-level or multilevel association rules.
- Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
- Rules at high concept level may add to common sense while rules at low concept level may not be useful always.
- Using uniform minimum support for all levels:
- When a uniform minimum support threshold is used, the search procedure is simplified.
- The method is also simple, in that users are required to specify only one minimum support threshold.
- The same minimum support threshold is used when mining at each level of abstraction.
- For example, in Figure, a minimum support threshold of 5% is used throughout.
- (e.g. for mining from “computer” down to “laptop computer”).
Both “computer” and “laptop computer” are found to be frequent, while “desktop computer” is not.
Using reduced minimum support at lower levels:
- Each level of abstraction has its own minimum support threshold.
- The deeper the level of abstraction, the smaller the corresponding threshold is.
- For example in Figure, the minimum support thresholds for levels 1 and 2 are 5% and 3%, respectively.
- In this way, “computer,” “laptop computer,” and “desktop computer” are all considered frequent.
Multilevel Association rule consists of alternate search strategies and Controlled level cross filtering:
1.Alternate Search Strategies:
Level by level independent:
- Full breadth search.
- No background knowledge in pruning.
- Leads to examine lot of infrequent items.
Level-cross filtering by single item:
- Examine nodes at level i only if node at level (i-1) is frequent.
- Misses frequent items at lower level abstractions (due to reduced support).
- Level-cross filtering by k-item set:
- Examine k-itemsets at level i only if k-itemsets at level (i-1) is frequent.
- Misses frequent k-itemsets at lower level abstractions (due to reduced support).
- Controlled Level-cross filtering by single item:
- A modified level-cross filtering by single item.
- Sets a level passage threshold for every level.
- Allows the inspection of lower abstractions even if its ancestor fails to satisfy min_sup threshold.
MULTIDIMENSIONAL ASSOCIATION RULES:
1.In Multi dimensional association:
- Attributes can be categorical or quantitative.
- Quantitative attributes are numeric and incorporates hierarchy.
- Numeric attributes must be discretized.
- Multi dimensional association rule consists of more than one dimension:
Eg: buys(X,”IBM Laptop computer”)buys(X,”HP Inkjet Printer”)
2.Three approaches in mining multi dimensional association rules:
1.Using static discritization of quantitative attributes.
- Discritization is static and occurs prior to mining.
- Discritized attributes are treated as categorical.
- Use apriori algorithm to find all k-frequent predicate sets(this requires k or k+1 table scans ).
- Every subset of frequent predicate set must be frequent.
- Eg: If in a data cube the 3D cuboid (age, income, buys) is frequent implies (age, income), (age, buys), (income, buys) are also frequent.
- Data cubes are well suited for mining since they make mining faster.
- The cells of an n-dimensional data cuboid correspond to the predicate cells.
2.Using dynamic discritization of quantitative attributes:
- Known as mining Quantitative Association Rules.
- Numeric attributes are dynamically discretized.
- Eg: age(X,”20..25”) Λ income(X,”30K..41K”)buys (X,”Laptop Computer”)
GRID FOR TUPLES
3.Using distance based discritization with clustering.
This id dynamic discretization process that considers the distance between data points.
- It involves a two step mining process:
- Perform clustering to find the interval of attributes involved.
- Obtain association rules by searching for groups of clusters that occur together.
- The resultant rules may satisfy:
- Clusters in the rule antecedent are strongly associated with clusters of rules in the consequent.
- Clusters in the antecedent occur together.
- Clusters in the consequent occur together.