Geometric Clustering of Multimedia Databases

Many kinds of texts are now available in various types of databases, and it has been requested to develop new methods to fully utilize them in a wide range of applications. In the field of information retrieval of full text databases, the vector-space model has been developed over 20 years (Salton et al. [14, 13]), and further the so-called latent semantic indexing based on the singular value decomposition of the corresponding matrix in the vector-space model has been demonstrated to find latent semantics (e.g., see Berry and Dumais [2]). This kind of geometric model has also be used in image databases (e.g., see Faloutsos et al [4]). This paper investigates the problem of learning some useful classifications in these databases as an unsupervised learning in such geometric setting. We clarify underlying geometric structures for them, and, based on the distances (similarity/dissimilarity measures) in the abovementioned existing research, propose using geometric clustering algorithms for..