A Survey on Soft Subspace Clustering <s> (1) SYNCLUS <s> In the application of clustering methods to real world data sets, two problems frequently arise: (a) how can the various contributory variables in a specific battery be weighted so as to enhance some cluster structure that may be present, and (b) how can various alternative batteries be combined to produce a single clustering that "best" incorporates each contributory set. A new method is proposed (SYNCLUS, SYNthesized CLUStering) for dealing with these two problems. <s> BIB001 </s> A Survey on Soft Subspace Clustering <s> (1) SYNCLUS <s> Partitional clustering is an important part of cluster analysis. Cluster analysis can be considered as one of the the most important approaches to unsupervised learning. The goal of clustering is to find clusters from unlabeled data, which means that data belonging to the same cluster are as similar as possible, whereas data belonging to different clusters are as dissimilar as possible. Partitional clustering is categorized as a prototype-based model, i.e., each cluster can be represented by a prototype, leading to a concise description of the original data set. According to different definitions of prototypes, such as data point, hyperplane, and hypersphere, the clustering methods can be categorized into different types of clustering algorithms with various prototypes. Besides organizing these partitional clustering methods into such a unified framework, relations between some commonly used nonpartitional clustering methods and partitional clustering methods are also discussed here. We give a brief overview of clustering, summarize well-known partitional clustering methods, and discuss the major challenges and key issues of these methods. Simple numerical experiments using toy data sets are carried out to enhance the description of various clustering methods. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc. <s> BIB002
The Synthesized Clustering (SYNCLUS) algorithm is developed to deal with variable weighting in k-means clustering BIB001 . The algorithm is divided into two stages. Starting from an initial set of weights, SYNCLUS first employs k-means clustering to partition data into k clusters, followed by the estimation of a set of new weights for different features by optimizing a weighted mean-square stress-like cost function. The two stages iterate until convergence to an optimal set of weights is achieved. The algorithm is computationally intensive and very time-consuming BIB002 , making it not suitable for handling large data sets.