A framework for automated association mining over multiple databases

Warning The system is temporarily closed to updates for reporting purpose.

Çinicioğlu, Esma Nur and Ertek, Gürdal and Demirer, Deniz and Yörük, Ersin Hasan (2011) A framework for automated association mining over multiple databases. In: International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2011), Istanbul, Turkey

[thumbnail of A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases] PDF (A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases)
cinicioglu_et_al_INISTA_2011_IEEE.pdf
Restricted to Registered users only

Download (1MB) | Request a copy
[thumbnail of A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases] PDF (A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases)
cinicioglu_et_al_INISTA.pdf

Download (1MB)
[thumbnail of A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases] MS Word (A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases)
cinicioglu_et_al_INISTA_2011_docx.doc
Restricted to Repository staff only

Download (1MB) | Request a copy
[thumbnail of A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases] MS Word (A Framework for Automated Association Mining Over Multiple DatabasesA Framework for Automated Association Mining Over Multiple Databases)
cinicioglu_et_al_INISTA.doc
Restricted to Repository staff only

Download (1MB) | Request a copy

Abstract

Literature on association mining, the data mining methodology that investigates associations between items, has primarily focused on efficiently mining larger databases. The motivation for association mining is to use the rules obtained from historical data to influence future transactions. However, associations in transactional processes change significantly over time, implying that rules extracted for a given time interval may not be applicable for a later time interval. Hence, an analysis framework is necessary to identify how associations change over time. This paper presents such a framework, reports the implementation of the framework as a tool, and demonstrates the applicability of and the necessity for the framework through a case study in the domain of finance.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: association mining, association mining over multiple databases, association mining visualization, data mining, graph visualization
Subjects: T Technology > T Technology (General)
H Social Sciences > HF Commerce > HF5410-5417.5 Marketing. Distribution of products
H Social Sciences > HD Industries. Land use. Labor > HD0028 Management. Industrial Management
H Social Sciences > H Social Sciences (General)
H Social Sciences > HD Industries. Land use. Labor > HD0030.2 Electronic data processing. Information technology
Q Science > QA Mathematics > QA076 Computer software
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Manufacturing Systems Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Gürdal Ertek
Date Deposited: 07 Jan 2012 22:13
Last Modified: 26 Apr 2022 09:04
URI: https://research.sabanciuniv.edu/id/eprint/18126

Actions (login required)

View Item
View Item