Searching for structure in data with fuzzy clusters of variable dimensionality of feature subspaces
Adam Pedrycz, Fangyan Dong, Kaoru Hirota · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Structural relationships in data are revealed by methods of clustering and fuzzy clustering. In essence, clustering leads to the reduction of data. Dimensionality reduction comes as a complementary process in which we eliminate some features (attributes). This study introduces a concept of structure reduction which is guided by a criterion of structure retention. In particular, it is shown that each cluster could be described by a different subset of features so that finally the reduction leads to the local feature subspaces. By analyzing the resulting subspaces, one could gain a better insight into a nature of the contributing features and in this way identify subsets of the most meaningful ones. The reduction problem is formulated and formalized as a certain combinatorial optimization task whose solution is provided by means of particle swarm optimization.