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Hierarchical co-clustering: a new way to organize the music data
- In IEEE Transactions on Multimedia
, 2011
"... Abstract—In music information retrieval (MIR) an important research topic, which has attracted much attention recently, is the utilization of user-assigned tags, artist-related style, and mood labels, which can be extracted from music listening web sites, such as Last.fm ..."
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Abstract—In music information retrieval (MIR) an important research topic, which has attracted much attention recently, is the utilization of user-assigned tags, artist-related style, and mood labels, which can be extracted from music listening web sites, such as Last.fm
Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised
"... Clustering aims at grouping data objects into meaningful clusters using no (or only a small amount of) supervision. This thesis studies two major cluster-ing paradigms: density-based and semi-supervised clustering. Density-based clustering algorithms seek partitions with high-density areas of points ..."
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Clustering aims at grouping data objects into meaningful clusters using no (or only a small amount of) supervision. This thesis studies two major cluster-ing paradigms: density-based and semi-supervised clustering. Density-based clustering algorithms seek partitions with high-density areas of points (clusters that are not necessarily globular) separated by low-density areas that may con-tain noise objects. Semi-supervised clustering algorithms use a small amount of information about data to guide the clustering task. In the context of density-based clustering, we study (a) the validation of density-based clustering and (b) hierarchical density-based clustering. The validation of density-based clustering, i.e., the objective and quanti-tative assessment of clustering results, is one of the most challenging aspects of clustering. Numerous different relative validity criteria have been proposed for the validation of globular clusters. Not all data, however, are composed of globular clusters. We propose a relative density-based validation index, DBCV,