TY - JOUR
T1 - Discover the semantic topology in high-dimensional data
AU - Chiang, I. J.
PY - 2007/7
Y1 - 2007/7
N2 - 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.
AB - 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.
KW - Association rules
KW - Document clustering
KW - Hierarchical clustering
KW - Simplicial complex
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U2 - 10.1016/j.eswa.2006.05.033
DO - 10.1016/j.eswa.2006.05.033
M3 - Article
AN - SCOPUS:33845625115
VL - 33
SP - 256
EP - 262
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 1
ER -