BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
A Grouping Principle and Four Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Cluster Analysis Using a Shared Near Neighbours Approach
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Navigating massive data sets via local clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Image clustering based on a shared nearest neighbors approach for tagged collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Boosting image retrieval through aggregating search results based on visual annotations
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Multi-source RSC Model for Multiple Search Result Clustering
ICCEE '09 Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 01
Plant leaves morphological categorization with shared nearest neighbours clustering
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Object-based visual query suggestion
Multimedia Tools and Applications
| Hi-index | 0.00 |
Shared Nearest Neighbours (SNN) techniques are well known to overcome several shortcomings of traditional clustering approaches, notably high dimensionality and metric limitations. However, previous methods were limited to a single information source whereas such methods appear to be very well suited for heterogeneous data, typically in multi-modal contexts. In this paper, we propose a new technique to accelerate the calculation of shared neighbours and we introduce a new multi-source shared neighbours scheme applied to multi-modal image clustering. We first extend existing SNN-based similarity measures to the case of multiple sources and we introduce an original automatic source selection step when building candidate clusters. The key point is that each resulting cluster is built with its own optimal subset of modalities which improves the robustness to noisy or outlier information sources. We experiment our method in the scope of multi-modal search result clustering, visual search mining and subspace clustering. Experimental results on both synthetic and real data involving different information sources and several datasets show the effectiveness of our method.