A Survey on Soft Subspace Clustering <s> (3) AWA <s> We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm. <s> BIB001 </s> A Survey on Soft Subspace Clustering <s> (3) AWA <s> One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets. <s> BIB002 </s> A Survey on Soft Subspace Clustering <s> (3) AWA <s> This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data. <s> BIB003
The Attribute Weighting Algorithm (AWA) developed by Chan et al. BIB002 employed an objective function similar to that of the W-k-means algorithm BIB003 . However, the sharing weights  In fact, it is also clear from the objective function of AWA and that of AWFCM as discussed above BIB001 that AWA is a hard clustering version of the AWFCM algorithm.