In spatial data mining, a common task is the discovery of spatial association rules from spatial databases. We propose a distributed system named ARES that assists data miners in the complex process of extracting the association rules from a spatial database. We also face a common problem of association rules mining, that is the high number of discovered rules. This affects both efficiency of the data mining process and quality of the discovered rules. We propose some criteria to bias the search and to filter the discovered rules according to user’s interests. Finally, we show the applicability of our proposal to two different real world domains, namely, document image processing and geo-referenced analysis of census data. We illustrate and comment experimental results on a set of multi-page documents extracted by IEEE PAMI and on North-West England 1998 census data.
Mining interesting spatial association rules: two case studies
APPICE, ANNALISA;CECI, MICHELANGELO;MALERBA, Donato;
2004-01-01
Abstract
In spatial data mining, a common task is the discovery of spatial association rules from spatial databases. We propose a distributed system named ARES that assists data miners in the complex process of extracting the association rules from a spatial database. We also face a common problem of association rules mining, that is the high number of discovered rules. This affects both efficiency of the data mining process and quality of the discovered rules. We propose some criteria to bias the search and to filter the discovered rules according to user’s interests. Finally, we show the applicability of our proposal to two different real world domains, namely, document image processing and geo-referenced analysis of census data. We illustrate and comment experimental results on a set of multi-page documents extracted by IEEE PAMI and on North-West England 1998 census data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
