A Survey on Soft Subspace Clustering <s> (1) OVW-UAT <s> A method is developed which for a given objects by variables data matrix estimates weighted inter-object distances that are optimally suited for either an ultrametric or an additive tree representation. The effectiveness of the method is demonstrated on two synthetic data sets having a known tree structure and on one real data set. In the final section, some possible extensions of the present method are discussed. <s> BIB001 </s> A Survey on Soft Subspace Clustering <s> (1) OVW-UAT <s> -means partitioning. We also describe some new features and improvements to the algorithm proposed by De Soete. Monte Carlo simulations have been conducted using different error conditions. In all cases (i.e., ultrametric or additive trees, or K-means partitioning), the simulation results indicate that the optimal weighting procedure should be used for analyzing data containing noisy variables that do not contribute relevant information to the classification structure. However, if the data involve error-perturbed variables that are relevant to the classification or outliers, it seems better to cluster or partition the entities by using variables with equal weights. A new computer program, OVW, which is available to researchers as freeware, implements improved algorithms for optimal variable weighting for ultrametric and additive tree clustering, and includes a new algorithm for optimal variable weighting for K-means partitioning. <s> BIB002
The Optimal Variable Weighting for Ultrametric and Additive Tree (OVW-UAT) is a feature weighting strategy developed for hierarchical clustering methods to solve the variable weighting problems BIB001 . In this approach, two objective functions are developed to determine the weights for trees in ultrametric and additive forms respectively. Once the optimal variable weights are obtained, the resulting inter-object dissimilarities can be applied to any of the existing ultrametric or additive tree fitting procedures. Since hierarchical clustering methods are computationally complex, the OVW-UAT approach cannot handle large data sets efficiently. Makarenkov and Legendre extended the OVW-UAT approach to optimal variable weighting for the k-means clustering BIB002 . The simulation results showed that the method was effective for identifying important variables, but still not scalable to large data sets.