Detail buku

No BukuT.LN.04.02
UniversitasOsaka Prefecture University, JAPAN
PenulisToto Silitonga
PembimbingProf. Dr. Hidetomo Ichihashi
AbstrakABSTRAK INGGRIS : Fuzzy e- Means clustering algorithm is the popular clustering technique by the distance-based objective function method. By the addition of a regularizer and the kernel trick to a fuzzy counterpart of Gaussian mixture density models (GMM). This paper proposes a clustering al-gorithm in an extended high dimensional feature space. Unlike the global nonlinear approaches, GMM or its fuzzy counterpart is to model nonlinear structure with a collection, or mixture, of local linear sub-models of PCA. When the number of feature vectors and clusters are n and C respectively, this kernel approach can find up to C x n nonzero eigenvalues. A way to control the number of parameters in the mixture of probabilistic principal component analysis (PPCA) is adopted to reduce the numberof parameters. The algorithm provides a partitioning with flexible shape of clusters in original input data space.