Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
Membership Enhancement with Exponential Fuzzy Clustering for Collaborative Filtering
verfasst von : Kiatichai Treerattanapitak, Chuleerat Jaruskulchai
Erschienen in: Neural Information Processing. Theory and Algorithms
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
In Recommendation System, Collaborative Filtering by Clustering is a technique to predict interesting items from users with similar preferences. However, misleading prediction could be taken place by items with very rare ratings. These missing data could be considered as noise and influence the cluster’s centroid by shifting its position. To overcome this issue, we proposed a new novel fuzzy algorithm that formulated objective function with Exponential equation (XFCM) in order to enhance ability to assign degree of membership. XFCM embeds noise filtering and produces membership for noisy data differently to other Fuzzy Clustering. Thus the centroid is robust in the noisy environment. The experiments on Collaborative Filtering dataset show that centroid produced by XFCM is robust by the improvement of prediction accuracy 6.12% over Fuzzy C-Mean (FCM) and 9.14% over Entropy based Fuzzy C-Mean (FCME).