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Extracting Knowledge in Data Warehouses using Fuzzy AprioriTid

by Somaieh Goudarzvand, Ali Harounabadi, Mohammad Mansour Riahi Kashani
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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

@article{ 10.5120/ijca2015907454,
author = { Somaieh Goudarzvand, Ali Harounabadi, Mohammad Mansour Riahi Kashani },
title = { Extracting Knowledge in Data Warehouses using Fuzzy AprioriTid },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 12 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number12/23505-2015907454/ },
doi = { 10.5120/ijca2015907454 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:27:11.830633+05:30
%A Somaieh Goudarzvand
%A Ali Harounabadi
%A Mohammad Mansour Riahi Kashani
%T Extracting Knowledge in Data Warehouses using Fuzzy AprioriTid
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 12
%P 39-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Data warehouses Extracting knowledge Fuzzy AprioriTid

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