Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
The 3D vase museum: a new approach to context in a digital library
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
InfoGallery: informative art services for physical library spaces
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Bregman Clustering with Approximation Guarantees
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Discrete & Computational Geometry
Deriving TF-IDF as a fisher kernel
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
CyberWalk: a web-based distributed virtual walkthrough environment
IEEE Transactions on Multimedia
Information-theoretic term weighting schemes for document clustering
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Information-geometric lenses for multiple foci+contexts interfaces
SIGGRAPH Asia 2013 Technical Briefs
| Hi-index | 0.00 |
Mainstream approaches in the design of virtual libraries basically exploit the same ambient space as their physical twins. Our paper is an attempt to rather capture automatically the actual space on which the books live, and learn the virtual library as a non-linear book manifold. This tackles tantalizing questions, chief among which whether modeling should be static and book focused (e.g.using bag of words encoding) or dynamic and user focused (e.g. relying on what we define as a bag of readers encoding). Experiments on a real-world digital library display that the latter encoding is a serious challenger to the former. Our results also show that the geometric layers of the manifold learned bring sizeable advantages for retrieval and visualization purposes. For example, the topological layer of the manifold allows to craft Manifold association rules; experiments display that they bring dramatic improvements over conventional association rules built from the discrete topology of book sets.Improvements embrace each of the following major standpoints on association rule mining: computational, support, confidence, lift, and leverage standpoint.