Explain multi level association rules for transaction databases
Answers
Answer:
Explanation:
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
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.
Association practices formed from mining data at various levels of abstraction are called multiple-level or multilevel association rules.
- Multilevel association disciplines can be mined efficiently using theory hierarchies under a support-confidence structure.
- Rules at a high concept level may add to common sense, while regulations at a low concept level may not always be useful.
- Using uniform minimum support for all levels:
- When a consistent minimum maintenance threshold is used, the search scheme is clarified.
- The method is also simple, in that users are required to specify only one minimum support threshold.