کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
388609 | 660930 | 2007 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Discover the semantic topology in high-dimensional data
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Discovering the homogeneous concept groups in the high-dimensional data sets and clustering them accordingly are contemporary challenge. Conventional clustering techniques often based on Euclidean metric. However, the metric is ad hoc not intrinsic to the semantic of the documents. In this paper, we are proposing a novel approach, in which the semantic space of high-dimensional data is structured as a simplicial complex of Euclidean space (a hypergraph but with different focus). Such a simplicial structure intrinsically captures the semantic of the data; for example, the coherent topics of documents will appear in the same connected component. Finally, we cluster the data by the structure of concepts, which is organized by such a geometry.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 33, Issue 1, July 2007, Pages 256–262
Journal: Expert Systems with Applications - Volume 33, Issue 1, July 2007, Pages 256–262
نویسندگان
I-Jen Chiang,