Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences)
Increasing massive amount of data and using them to improve the quality of management decisions are the problems of libraries and information centers. In the meantime, a powerful data mining tool can analyze data to predict and analyze user transactions and stop wasting time to use useful data from...
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Iranian Research Institute for Information and Technology
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doaj-246a1b25a03e433f9cdea1bdb68790862020-11-25T02:55:44ZfasIranian Research Institute for Information and TechnologyIranian Journal of Information Processing & Management2251-82232251-82312019-07-0134311551186Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences)Nasim Ansari0Hossein Vakili mofrad1Muharram Mansoori zadeh2Mohamad reza Amiri3 school of Para Medicine, Hamadan University of Medical Sciences, Hamadan, Iran. Hamadan University of Medical Sciences, Hamadan, Iran. University of Bu-Ali Sina, Hamadan, Iran school of Para Medicine, Hamadan University of Medical Sciences, Hamadan, Iran. Increasing massive amount of data and using them to improve the quality of management decisions are the problems of libraries and information centers. In the meantime, a powerful data mining tool can analyze data to predict and analyze user transactions and stop wasting time to use useful data from among massive data, identify valid patterns and unknown relationships, and help libraries in strategic decision-making and effective management assistance. This study using association rules technique, which is one of the data mining methods, seeks to analyze the transactions of user lending and discover their access pattern. In this cross-sectional descriptive study required data were collected from transactions of users of the libraries and information centers of Hamedan University of Medical Sciences and after preprocessing these data, the total number of 6636 user transactions was 132833 using a census method during a three-year period examined. The results of this study showed that most transactions and referrals with the score of 20.7±24.4 were related to the students and the highest delay and duration of loan respectively with the score of 884.3±1396.9 and 885.7±1765.3 related to the faculty members. Also, the rules derived from the Apriori algorithm showed by providing a users access pattern based on demographic information and determining subject dependence of information resources could be used as an appropriate pattern for analyzing and predicting user transactions. Using association rules technique and implementing the Apriori algorithm, the best rules governing the data set were extracted. Therefore, using association rules technique can be designed as a book advisory system in libraries and information centers. Managers and policy-makers can also use these patterns and rules to align their supply and information resources with the real needs of their members, and can benefit greatly from library procurement, collection, management and services.http://jipm.irandoc.ac.ir/article-1-3975-en.htmldata miningassociation rulesapriori algorithmlibrary userslibrary circulation recordsacademic librarieshamedan university of medical sciences. |
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DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
Nasim Ansari Hossein Vakili mofrad Muharram Mansoori zadeh Mohamad reza Amiri |
spellingShingle |
Nasim Ansari Hossein Vakili mofrad Muharram Mansoori zadeh Mohamad reza Amiri Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences) Iranian Journal of Information Processing & Management data mining association rules apriori algorithm library users library circulation records academic libraries hamedan university of medical sciences. |
author_facet |
Nasim Ansari Hossein Vakili mofrad Muharram Mansoori zadeh Mohamad reza Amiri |
author_sort |
Nasim Ansari |
title |
Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences) |
title_short |
Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences) |
title_full |
Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences) |
title_fullStr |
Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences) |
title_full_unstemmed |
Discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (Case study: Libraries and Information Centers of Hamedan University of Medical Sciences) |
title_sort |
discovery of user access pattern and loan transactions analysis of information resources using association rules technique in data mining (case study: libraries and information centers of hamedan university of medical sciences) |
publisher |
Iranian Research Institute for Information and Technology |
series |
Iranian Journal of Information Processing & Management |
issn |
2251-8223 2251-8231 |
publishDate |
2019-07-01 |
description |
Increasing massive amount of data and using them to improve the quality of management decisions are the problems of libraries and information centers. In the meantime, a powerful data mining tool can analyze data to predict and analyze user transactions and stop wasting time to use useful data from among massive data, identify valid patterns and unknown relationships, and help libraries in strategic decision-making and effective management assistance. This study using association rules technique, which is one of the data mining methods, seeks to analyze the transactions of user lending and discover their access pattern. In this cross-sectional descriptive study required data were collected from transactions of users of the libraries and information centers of Hamedan University of Medical Sciences and after preprocessing these data, the total number of 6636 user transactions was 132833 using a census method during a three-year period examined. The results of this study showed that most transactions and referrals with the score of 20.7±24.4 were related to the students and the highest delay and duration of loan respectively with the score of 884.3±1396.9 and 885.7±1765.3 related to the faculty members. Also, the rules derived from the Apriori algorithm showed by providing a users access pattern based on demographic information and determining subject dependence of information resources could be used as an appropriate pattern for analyzing and predicting user transactions. Using association rules technique and implementing the Apriori algorithm, the best rules governing the data set were extracted. Therefore, using association rules technique can be designed as a book advisory system in libraries and information centers. Managers and policy-makers can also use these patterns and rules to align their supply and information resources with the real needs of their members, and can benefit greatly from library procurement, collection, management and services. |
topic |
data mining association rules apriori algorithm library users library circulation records academic libraries hamedan university of medical sciences. |
url |
http://jipm.irandoc.ac.ir/article-1-3975-en.html |
work_keys_str_mv |
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