BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Intelligent crawling on the World Wide Web with arbitrary predicates
Proceedings of the 10th international conference on World Wide Web
Accelerated focused crawling through online relevance feedback
Proceedings of the 11th international conference on World Wide Web
Focused Crawling Using Context Graphs
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
An Active Set Method for Single-Cone Second-Order Cone Programs
SIAM Journal on Optimization
Clustering based large margin classification: a scalable approach using SOCP formulation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper we propose a novel, scalable, clustering based Ordinal Regression formulation, which is an instance of a Second Order Cone Program (SOCP) with one Second Order Cone (SOC) constraint. The main contribution of the paper is a fast algorithm, CB-OR, which solves the proposed formulation more eficiently than general purpose solvers. Another main contribution of the paper is to pose the problem of focused crawling as a large scale Ordinal Regression problem and solve using the proposed CB-OR. Focused crawling is an efficient mechanism for discovering resources of interest on the web. Posing the problem of focused crawling as an Ordinal Regression problem avoids the need for a negative class and topic hierarchy, which are the main drawbacks of the existing focused crawling methods. Experiments on large synthetic and benchmark datasets show the scalability of CB-OR. Experiments also show that the proposed focused crawler outperforms the state-of-the-art.