DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
This work developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories, and evaluated the most accurate SCL predictors using 5-fold cross validation plus an independent proteomics analysis.
A model-based approach for recommendation in social networks, employing matrix factorization techniques and incorporating the mechanism of trust propagation into the model demonstrates that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
A novel pruning strategy is designed based on these concepts, which guarantees the precision of the weights of the micro-clusters with limited memory, and demonstrates the effectiveness and efficiency of the method.
It is demonstrated that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components, and that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics.
The generalized algorithm DBSCAN can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes, and four applications using 2D points (astronomy, 3D points,biology, 5D points and 2D polygons) are presented, demonstrating the applicability of GDBSCAN to real-world problems.
A random walk model combining the trust-based and the collaborative filtering approach for recommendation is proposed, which allows us to define and to measure the confidence of a recommendation.
This paper proposes to use the notion of frequent itemsets, which comes from association rule mining, for document clustering, and shows that this method outperforms best existing methods in terms of both clustering accuracy and scalability.
Two algorithms for frequent term-based text clustering are presented, FTC which creates flat clusterings and HFTC for hierarchical clustering, which obtain clusterings of comparable quality significantly more efficiently than state-of-the- artText clustering algorithms.