USING GRAPHS IN MULTI RELATIONAL DATA MINING

Yıl 2015, Cilt: 11 Sayı: 1, 19 - 33, 20.01.2016

Mahmut İğde Yusuf Kavurucu Alev Mutlu

Öz

Multi-relational concept discovery aims to find the relational rules that best describe the target concept. In this paper, we present a graph-based concept discovery method in Multi- Relational Data Mining. Concept rule discovery aims at finding the definition of a specific concept in terms of relations involving background knowledge. The proposed method is an improvement over a state-of-the-art concept discovery system that uses both ILP and conventional association rule mining techniques during concept discovery process. The proposed method generates graph structures with respect to data that is initially stored in a relational database and utilizes them to guide the concept induction process. A set of experiments is conducted on data sets that belong to different learning problems. The results show that the proposed method has promising results in comparison to state of the art methods.

Anahtar Kelimeler

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ÇOKLU İLİŞKİSEL VERİ MADENCİLİĞİNDE GRAFİKLERİN KULLANIMI

Öz

Çok ilişkili konsept keşfinin amacı hedef konsepti en iyi şekilde anlatabilen ilşkisel
kuralları bulmaktır. Bu çalışma ile çok ilişkili veri madenciliğinde diyagram tabanlı konsept
keşif metodundan bahsediyoruz. Konsept kural keşfi, arkaplan bilgilerini içeren ilişkileri
gözönünde bulundurarak özel bir konsetin tanımını bulmayı hedefler. Anlatılan metot C^2D konsept keşif sisteminin geliştirlmesi ile elde edilmiştir. C^2D konsept keşfi esnasında ILP ve
geleneksel ortaklık kural madenciliği (APRIORI gibi) tekniklerini birlikte kullanır. Anlatılan
sistem, isim olarak D-KKS(Diyagram tabanlı Konsept Keşif Sistemi), başlangıçta ilişkisel
veritabanında tutulan verilere bağlı kalmak kaydı ile diyagram yapılarını oluşturur ve bu
verileri kullanarak konsept çıkarsama sürecini yönlendirir.Farklı öğrenme problemleri ile
alakalı veri setleri üzerinde testler yapılmıştır. Test sonuçları D-KKS'nin, bu alandaki diğer
sistemler ve C^2D'ye nispeten umut verici olduğunu göstermektedir.

Anahtar Kelimeler

Konsept Keşfi, Grafik, Yol, Çok İlişili Veri Madenciliği, Tümevaran

Kaynakça

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