Detail publikace

Framework for mining of association rules from data warehouse

Originální název

Framework for mining of association rules from data warehouse

Anglický název

Framework for mining of association rules from data warehouse

Jazyk

en

Originální abstrakt

In this paper, we propose a framework for association rules mining from data warehouses. This framework presents alliance between two business intelligence areas. First area is represented by data warehouse and data cube providing high quality data. The second area is represented by data mining, especially association rules mining providing an additional knowledge. Association rules mining on data warehouses is different from mining on relational or transactional databases, because it deals with couple of dimensions, which form conceptual hierarchies. Thus we mine multi- and inter-dimensional association rules. There are several approaches how to mine such association rules described in literature. This framework presents a novel combination of the data cube processing - top-down (on product dimensions) and bottom-up (on domain dimensions). We presume division of dimensions on domain and product dimensions. The framework works in the following steps.  The first one represents obtaining frequent leaf 1-itemsets, which means obtaining frequent itemsets from domains represented by items from domain dimensions on leaf level. In the second step we obtain all frequent 1-itemset. Following step represents iterative mining of frequent k-itemset from frequent (k-1)-itemsets. In the final step we process all k-itemsets and obtain association rules from them.

Anglický abstrakt

In this paper, we propose a framework for association rules mining from data warehouses. This framework presents alliance between two business intelligence areas. First area is represented by data warehouse and data cube providing high quality data. The second area is represented by data mining, especially association rules mining providing an additional knowledge. Association rules mining on data warehouses is different from mining on relational or transactional databases, because it deals with couple of dimensions, which form conceptual hierarchies. Thus we mine multi- and inter-dimensional association rules. There are several approaches how to mine such association rules described in literature. This framework presents a novel combination of the data cube processing - top-down (on product dimensions) and bottom-up (on domain dimensions). We presume division of dimensions on domain and product dimensions. The framework works in the following steps.  The first one represents obtaining frequent leaf 1-itemsets, which means obtaining frequent itemsets from domains represented by items from domain dimensions on leaf level. In the second step we obtain all frequent 1-itemset. Following step represents iterative mining of frequent k-itemset from frequent (k-1)-itemsets. In the final step we process all k-itemsets and obtain association rules from them.

BibTex


@inproceedings{BUT29550,
  author="Lukáš {Stryka} and Petr {Chmelař}",
  title="Framework for mining of association rules from data warehouse",
  annote="In this paper, we propose a framework for association rules mining from data
warehouses. This framework presents alliance between two business intelligence
areas. First area is represented by data warehouse and data cube providing high
quality data. The second area is represented by data mining, especially
association rules mining providing an additional knowledge.
Association rules mining on data warehouses is different from mining on
relational or transactional databases, because it deals with couple of
dimensions, which form conceptual hierarchies. Thus we mine multi- and
inter-dimensional association rules. There are several approaches how to mine
such association rules described in literature. This framework presents a novel
combination of the data cube processing - top-down (on product dimensions) and
bottom-up (on domain dimensions). We presume division of dimensions on domain and
product dimensions. 
The framework works in the following steps.  The first one represents obtaining
frequent leaf 1-itemsets, which means obtaining frequent itemsets from domains
represented by items from domain dimensions on leaf level. In the second step we
obtain all frequent 1-itemset. Following step represents iterative mining of
frequent k-itemset from frequent (k-1)-itemsets. In the final step we process all
k-itemsets and obtain association rules from them.",
  address="The University of Technology Košice",
  booktitle="ITAT 2008",
  chapter="29550",
  edition="NEUVEDEN",
  howpublished="print",
  institution="The University of Technology Košice",
  year="2008",
  month="september",
  pages="95--98",
  publisher="The University of Technology Košice",
  type="conference paper"
}