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Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching
Andrew McCallum WhizBang! Labs - Research 4616 Henry Street Pittsburgh, PA...

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Abstract:
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Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of efficiently clustering datasets that are large in all three ways at once--for example, having millions of data points that exist in many thousands of dimensions representing many thousands of clusters. We present a new technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently...

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BibTeX entry:
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@misc{ whizbang-efficient,
  author = "Andrew Mccallum Whizbang",
  title = "Efficient Clustering of High-Dimensional Data Sets with Application to
    Reference Matching" }


Citations (may not include all citations):

1   On entropy maximization principle (context) - Akaike


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