Algorithms for clustering data
Algorithms for clustering data
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Clustering Algorithms
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Building implicit links from content for forum search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning on k-partite graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
Hierarchical co-clustering based on entropy splitting
Proceedings of the 21st ACM international conference on Information and knowledge management
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Music artist similarity has been an active research topic in music information retrieval for a long time since it is especially useful for music recommendation and organization. However, it is a difficult problem. The similarity varies significantly due to different artistic aspects considered and most importantly, it is hard to quantify. In this paper, we propose a new framework for quantifying artist similarity. In the framework, we focus on style and mood aspects of artists whose descriptions are extracted from the authoritative information available at the All Music Guide website. We then generate style--mood joint taxonomies using hierarchical co-clustering algorithm, and quantify the semantic similarities between the style/mood terms based on the taxonomy structure and the positions of these terms in the taxonomies. Finally we calculate the artist similarities according to all the style/mood terms used to describe them. Experiments are conducted to show the effectiveness of our framework.