What is constraint based association mining explain with an example?
What is constraint based association mining explain with an example?
CONSTRAINT BASED ASSOCIATION RULES: A data mining process may uncover thousands of rules from a given set of data, most of which end up being unrelated or uninteresting to the users. Thus, a good heuristic is to have the users specify such intuition or expectations as constraints to confine the search space.
What is constraint based association mining?
Constraint-based mining is the research area studying the development of data mining algorithms that search through a pattern or model space restricted by constraints. The term is usually used to refer to algorithms that search for patterns only.
What is useful in constraint based association mining?
“How are metarules useful?” Metarules allow users to specify the syntactic form of rules that they are interested in mining. The rule forms can be used as constraints to help improve the efficiency of the mining process.
What is association rule mining with example?
A classic example of association rule mining refers to a relationship between diapers and beers. The example, which seems to be fictional, claims that men who go to a store to buy diapers are also likely to buy beer. Of those, about 3,500 transactions, 1.75%, include both the purchase of diapers and beer.
What are the various kinds of association rules?
Multidimensional association rules with no repeated predicates are called inter dimensional association rules. We can also mine multidimensional association rules with repeated predicates, which contain multiple occurrences of some predicates. These rules are called hybrid-dimensional association rules.
What is multilevel association rules?
Association rules created from mining information at different degrees of reflection are called various level or staggered association rules. Multilevel association rules can be mined effectively utilizing idea progressions under a help certainty system.
How are Metarules useful in mining of association rules?
A meta-rule-guided data mining approach is proposed and studied which applies meta-rules as a guidance at finding multiple-level association rules in large relational databases. A meta-rule is a rule template in the form of “P1 ² . . . interface which specifi es the set of data relevant to a particular mining task.
What are the applications of association rule mining?
Applications of association rule mining are stock analysis, web log mining, medical diagnosis, customer market analysis bioinformatics etc. In past, many algorithms were developed by researchers for Boolean and Fuzzy association rule mining such as Apriori, FP-tree, Fuzzy FP-tree etc.
What is the aim of association rule mining?
Given a set of transactions, association rule mining aims to find the rules which enable us to predict the occurrence of a specific item based on the occurrences of the other items in the transaction.
What are the types of association?
The three types of associations (chance, non-causal, and causal).
What is Multilevel Association Rule explain with example?
What are the multidimensional and multilevel association rules?
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.
What does constraint-based association rule mining call for?
This calls for constraint-based association rule mining. There are two aspects of interestingness of rules that have been studied in data mining literature, objective and subjective measures (Liu et al….
How are metarules useful in association mining process?
Metarule-Guided Mining of Association Rules “How are metarules useful?” Metarules allow users to specify the syntactic form of rules that they are interested in mining. The rule forms can be used as constraints to help improve the efficiency of the mining process.
Which is a good strategy for constraint based mining?
Often, users have a good sense of which “direction” of mining may lead to interesting patterns and the “form” of the patterns or rules they would like to find. Thus, a good heuristic is to have the users specify such intuition or expectations as constraints to confine the search space. This strategy is known as constraint-based mining.
How are dimension and level constraints used in rule mining?
Dimension/level constraints: These specify the desired dimensions (or attributes) of the data, or levels of the concept hierarchies, to be used in mining. Interestingness constraints: These specify thresholds on statistical measures of rule interestingness, such as support, confidence, and correlation.
