The other thresholds in the mining association algorithm.

• Autorzy: Kawahara M. , Kawano H
• Języki publikacji: EN

Abstrakty

EN

The mining association algorithm is one of the most important data mining algorithms to derive association rules at high speed from huge databases. However, the rules derived by the algorithm contain many noises; it is then necessary for some systems to remove the noises using noise filters such as stopword lists. We improve the algorithm and develop bibliographic navigation systems using the algorithms, and we also use a dictionary to remove noises in association rules. In order to derive effective rules, it is very important to determine system parameters, such as Minsup and Minconf threshold values. We adapt the receiver operating characteristic analysis on the algorithm in our bibliographic navigation system and evaluate the performance of the derived rules. We propose extended mining association algorithms. Moreover, we evaluate the performance of our algorithms proposed using experimental database and show how our algorithms can derive effective association rules. We also show that our algorithm can remove stopwords from raw data automatically.

Słowa kluczowe

EN

mining association algorithm; data mining; association rules; noise filters; stopword lists; bibliographic navigation; threshold values; receiver operating characteristic; database; Minsup; Minconf

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2000
• Tom: Vol. 26, nr 2
• Strony: 95--109
• Opis fizyczny: Bibliogr. 10 poz.,tab. 2, rys. 8,

Twórcy

autor

Kawahara M.

autor

Kawano H