Contextual association rules represent co-occurrences between contexts and properties of data, where the context is a set of environmental or user personal features employed to customize an application. Due to their particular structure, these rules can be very tricky to mine, and if the process is not carried out with care, an unmanageable set of not significant rules may be extracted. In this paper we survey two existing algorithms for relational databases and present a novel algorithm that merges the two proposals overcoming their limitations
We discuss the use of database methods for data mining. Recently impressive results have been achiev...
Abstract. Association rule mining is an important mining function be-cause of its completeness. It h...
Association rules (AR) represent one of the most powerful and largely used approaches to detect the ...
Contextual association rules represent co-occurrences between contexts and properties of data, where...
Association rules are generally recognized as a highly valuable type of regularities and various alg...
One of the primary goals of data mining is to extract patterns from a large volume of data. Rules ch...
A meta-rule-guided data mining approach is proposed and studied which applies meta-rules as a guidan...
In traditional classification setting, training data are represented as a single table, where each r...
Mining association rules from a large collection of databases is based on two main tasks. One is gen...
In this paper we address the problem of finding all association rules in tabular data. An algorithm,...
Data is mostly stored in relational databases today. However, most data mining algorithms are not ca...
This paper will contain a comparison of popular methods o f discovering association rules between it...
multidimensional association rules with non repetitive predicate from Relational Database is given. ...
In today’s world, the amount of data transfer has been increasing in a fast pace in all fields due t...
Relational association rules reveal patterns hide in multiple tables. Existing rules are usually eva...
We discuss the use of database methods for data mining. Recently impressive results have been achiev...
Abstract. Association rule mining is an important mining function be-cause of its completeness. It h...
Association rules (AR) represent one of the most powerful and largely used approaches to detect the ...
Contextual association rules represent co-occurrences between contexts and properties of data, where...
Association rules are generally recognized as a highly valuable type of regularities and various alg...
One of the primary goals of data mining is to extract patterns from a large volume of data. Rules ch...
A meta-rule-guided data mining approach is proposed and studied which applies meta-rules as a guidan...
In traditional classification setting, training data are represented as a single table, where each r...
Mining association rules from a large collection of databases is based on two main tasks. One is gen...
In this paper we address the problem of finding all association rules in tabular data. An algorithm,...
Data is mostly stored in relational databases today. However, most data mining algorithms are not ca...
This paper will contain a comparison of popular methods o f discovering association rules between it...
multidimensional association rules with non repetitive predicate from Relational Database is given. ...
In today’s world, the amount of data transfer has been increasing in a fast pace in all fields due t...
Relational association rules reveal patterns hide in multiple tables. Existing rules are usually eva...
We discuss the use of database methods for data mining. Recently impressive results have been achiev...
Abstract. Association rule mining is an important mining function be-cause of its completeness. It h...
Association rules (AR) represent one of the most powerful and largely used approaches to detect the ...