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Extracting Knowledge in Data Warehouses using Fuzzy AprioriTid
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 131 - Number 12 |
| Year of Publication: 2015 |
| Authors: Somaieh Goudarzvand, Ali Harounabadi, Mohammad Mansour Riahi Kashani |
10.5120/ijca2015907454
|
Somaieh Goudarzvand, Ali Harounabadi, Mohammad Mansour Riahi Kashani . Extracting Knowledge in Data Warehouses using Fuzzy AprioriTid. International Journal of Computer Applications. 131, 12 ( December 2015), 39-43. DOI=10.5120/ijca2015907454
Abstract
Multidimensional databases and OLAP tools that provide an efficient framework for data mining have been pushing us to the OLAM architecture. OLAP is widely used to illustrate meaningful and interactive analysis of data on the complex structure. In contrast, detecting hidden patterns in the data and exploring them is for the data mining. OLAP and data mining are believed to complete each other for analyzing large data sets in decision support systems efficiently. Unlike previous work in this field, this method does not rely on the availability of knowledge in a particular field. Variables will be selected with the consideration of user to build cubes. Hierarchical clustering is used to obtain dynamic relationships between variables at different levels of data. Results of the Adult data set shows that the obtained Lift from Fuzzy AprioriTid compared with Apriori algorithm increased.
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