Abstract. In this paper we propose a novel (multi-)relational classification framework based on propositionalization. Propositionalization makes use of discovered relational association rules and permits to significantly reduce feature space through a feature reduction algorithm. The method is implemented in a Data Mining system tightly integrated with a relational database. It performs the classification at different granularity levels and takes advantage from domain specific knowledge in form of rules that support qualitative reasoning. An application of classification in real-world geo-referenced census data analysis is reported.
One of the primary goals of data mining is to extract patterns from a large volume of data. Rules ch...
Abstract. Currently statistical and artificial neural network methods dominate in data mining applic...
In this paper we propose a method for the discovery of spatial association rules, that is, associati...
In this paper we propose a novel (multi-)relational classification framework based on propositional...
Abstract. In traditional classification setting, training data are represented as a single table, wh...
Data is mainly available in relational formats, so relational data mining receives a lot of interes...
Companies want to extract value from their relational databases. This is the aim of relational data ...
Mining of association rules is of interest to data miners. Typically, before association rules are m...
In this paper we propose a method for the discovery of spatial association rules, that is, associati...
summary:Systems aiming at discovering interesting knowledge in data, now commonly called data mining...
Census data mining has great potential both in business development and in good public policy, but s...
Abstract Relational databases are the most popular repository for structured data, and are thus one ...
For many real-world applications it is important to choose the right representation language. While ...
Propositionalization, Inductive Logic Programming, Multi-Relational Data MiningMagdeburg, Univ., Fak...
This book provides two general granular computing approaches to mining relational data, the first of...
One of the primary goals of data mining is to extract patterns from a large volume of data. Rules ch...
Abstract. Currently statistical and artificial neural network methods dominate in data mining applic...
In this paper we propose a method for the discovery of spatial association rules, that is, associati...
In this paper we propose a novel (multi-)relational classification framework based on propositional...
Abstract. In traditional classification setting, training data are represented as a single table, wh...
Data is mainly available in relational formats, so relational data mining receives a lot of interes...
Companies want to extract value from their relational databases. This is the aim of relational data ...
Mining of association rules is of interest to data miners. Typically, before association rules are m...
In this paper we propose a method for the discovery of spatial association rules, that is, associati...
summary:Systems aiming at discovering interesting knowledge in data, now commonly called data mining...
Census data mining has great potential both in business development and in good public policy, but s...
Abstract Relational databases are the most popular repository for structured data, and are thus one ...
For many real-world applications it is important to choose the right representation language. While ...
Propositionalization, Inductive Logic Programming, Multi-Relational Data MiningMagdeburg, Univ., Fak...
This book provides two general granular computing approaches to mining relational data, the first of...
One of the primary goals of data mining is to extract patterns from a large volume of data. Rules ch...
Abstract. Currently statistical and artificial neural network methods dominate in data mining applic...
In this paper we propose a method for the discovery of spatial association rules, that is, associati...