Multi-view clustering. Bickel, S. & Scheffer, T. In Fourth IEEE International Conference on Data Mining (ICDM'04), pages 19–26, November, 2004. doi abstract bibtex We consider clustering problems in which the available attributes can be split into two independent subsets, such that either subset suffices for learning. Example applications of this multi-view setting include clustering of Web pages which have an intrinsic view (the pages themselves) and an extrinsic view (e.g., anchor texts of inbound hyperlinks); multi-view learning has so far been studied in the context of classification. We develop and study partitioning and agglomerative, hierarchical multi-view clustering algorithms for text data. We find empirically that the multi-view versions of k-means and EM greatly improve on their single-view counterparts. By contrast, we obtain negative results for agglomerative hierarchical multi-view clustering. Our analysis explains this surprising phenomenon.
@inproceedings{bickel_multi-view_2004,
title = {Multi-view clustering},
doi = {10.1109/ICDM.2004.10095},
abstract = {We consider clustering problems in which the available attributes can be split into two independent subsets, such that either subset suffices for learning. Example applications of this multi-view setting include clustering of Web pages which have an intrinsic view (the pages themselves) and an extrinsic view (e.g., anchor texts of inbound hyperlinks); multi-view learning has so far been studied in the context of classification. We develop and study partitioning and agglomerative, hierarchical multi-view clustering algorithms for text data. We find empirically that the multi-view versions of k-means and EM greatly improve on their single-view counterparts. By contrast, we obtain negative results for agglomerative hierarchical multi-view clustering. Our analysis explains this surprising phenomenon.},
booktitle = {Fourth {IEEE} {International} {Conference} on {Data} {Mining} ({ICDM}'04)},
author = {Bickel, S. and Scheffer, T.},
month = nov,
year = {2004},
keywords = {Data mining},
pages = {19--26},
}
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