Graph-based multimodal clustering for social multimedia
2017
Real world datasets often consist of data expressed through multiple modalities. Clustering such datasets is in most cases a challenging task as the involved modalities are often heterogeneous. In this paper we propose a graph-based multimodal clustering approach. The proposed approach utilizes an example relevant clustering in order to learn a model of the "same cluster" relationship between a pair of items. This model is subsequently used in order to organize the items of the collection to be clustered in a graph, where the nodes represent the items and a link between a pair of nodes exists if the model predicted that the corresponding pair of items belong to the same cluster. Eventually, a graph clustering algorithm is applied on the graph in order to produce the final clustering. The proposed approach is applied on two problems that are typically treated using clustering techniques; in particular, it is applied on the problem of detecting social events and to the problem of discovering different landmark views in collections of social multimedia.
Keywords:
- Artificial intelligence
- Correlation clustering
- Fuzzy clustering
- Constrained clustering
- Pattern recognition
- Computer science
- FLAME clustering
- Canopy clustering algorithm
- Machine learning
- Cluster analysis
- Multimedia
- Brown clustering
- CURE data clustering algorithm
- Clustering coefficient
- Data stream clustering
- Data mining
- Correction
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