Anzeige
Erschienen in:
2005 | OriginalPaper | Buchkapitel
Mining Association Rules Based on Seed Items and Weights
verfasst von : Chen Xiang, Zhang Yi, Wu Yue
Erschienen in: Fuzzy Systems and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The traditional algorithms of mining association rules, such as
Apriori
, often suffered from the bottleneck of itemset generation because the database is too large or the threshold of minimum support is not suitable. Furthermore, the traditional methods often treated each item evenly. It resulted in some problems. In this paper, a new algorithm to solve the above problems is proposed. The approach is to replace the database with the base set based on some seed items and assign weights to each item in the base set. Experiments on performance study will prove the superiority of the new algorithm.