The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Reference-based indexing of sequence databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining relationships among interval-based events for classification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ARTEMIS: assessing the similarity of event-interval sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
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Link analysis ranking methods are widely used for summarizing the connectivity structure of large networks. We explore a weighted version of two common link analysis ranking algorithms, PageRank and HITS, and study their applicability to assistive environment data. Based on these methods, we propose a novel approach for identifying representative objects in large datasets, given their similarity matrix. The novelty of our approach is that it takes into account both the pair-wise similarities between the objects, as well as the origin and "evolution path" of these similarities within the dataset. The key step of our method is to define a complete graph, where each object is represented by a node and each edge in the graph is given a weight equal to the pairwise similarity value of the two adjacent nodes. Nodes with high ranking scores correspond to representative objects. Our experimental evaluation was performed on three data domains: american sign language, sensor data, and medical data.