LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering cluster-based local outliers
Pattern Recognition Letters
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Improving Prediction Quality in Collaborative Filtering Based on Clustering
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A modified fuzzy C-means algorithm for collaborative filtering
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Entropy based fuzzy C-Mean for item-based collaborative filtering
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Possibilistic approach to kernel-based fuzzy c-means clustering with entropy regularization
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Generalized agglomerative fuzzy clustering
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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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).