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Article
Enhancement of Association Rules Interpretability using Generalization

Authors: Safaa O. Al-Mamory --- Zahraa Najim Abdullah
Journal: Journal of University of Babylon مجلة جامعة بابل ISSN: 19920652 23128135 Year: 2017 Volume: 25 Issue: 3 Pages: 774-790
Publisher: Babylon University جامعة بابل

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Abstract

Data mining has a number of common methods. One of such methods is the association rules mining. While association rules mining often produces huge number of rules, it prevents the analyst from finding interesting rules and consequently, this method is a waste of time. Visualization is one of the methods to solve such problems. However, most of the association rule visualization techniques are suffering from viewing huge number of rules. This paper provides a modification on the techniques of the visualization to help the analyst to interpret the association rules by grouping the large number of rules using a modified Attribute Oriented Induction algorithm, then; these grouped rules are visualized using a grouped graph method. Experimental results show that the proposed technique produces excellent compression ratio.

تعدين البيانات يمتلك عدد من الطرق الشائعة، وان تعدين قواعد الاقتران هي احدى تلك الطرق. بما ان تعدين قواعد الاقتران ينتج كمية هائلة من القواعد فأنه يمنع المحلل من ايجاد القواعد المهمة، وبالتالي فان هذه العملية تعتبر تضييع للوقت. العرض هو احدى الطرق لحل هكذا مشاكل. وبما أن معظم تقنيات عرض قواعد الاقتران تعاني من عرض عدد هائل من القواعد، فأن هذا البحث يوفر تعديل لتقنيات العرض لتساعد المحلل لتفسير قواعد الاقتران بواسطة تجميع عدد كبير من القواعد باستخدام خوارزمية الاستقراء الموجه للصفات المعدلة و من ثم عرض تلك القواعد المجمعة باستخدام طريقة الرسم البياني المجمع , وقد أوضحت النتائج ان التقنية المقترحة تنتج نسبة ضغط ممتازة.


Article
Combining the Attribute Oriented Induction and Graph Visualization to Enhancement Association Rules Interpretation

Authors: Safaa O. Al-Mamory د. صفاء عبيس المعموري --- Zahraa Najim Abdullah زهراء نجم عبدالله
Journal: Iraqi Journal for Computers and Informatics ijci المجلة العراقية للحاسبات والمعلوماتية ISSN: 2313190X 25204912 Year: 2016 Volume: 42 Issue: 1 Pages: 10-22
Publisher: University Of Informatics Technology And Communications جامعة تكنولوجيا المعلومات و الاتصالات

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Abstract

The important methods of data mining is large andfrom these methods is mining of association rule. The miningof association rule gives huge number of the rules. These hugerules make analyst consuming more time when searchingthrough the large rules for finding the interesting rules. One ofthe solutions for this problem is combing between one of theAssociation rules visualization method and generalizationmethod. Association rules visualization method is graph-basedmethod. Generalization method is Attribute OrientedInduction algorithm (AOI). AOI after combing calls ModifiedAOI because it removes and changes in the steps of thetraditional AOI. The graph technique after combing also callsgrouped graph method because it displays the aggregated thatresults rules from AOI. The results of this paper are ratio ofcompression that gives clarity of visualization. These resultsprovide the ability for test and drill down in the rules orunderstand and roll up.

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