Comparison of incomplete data handling techniques for neuro-fuzzy system

Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the input domain. The most popular approach (also used in the ANNBFIS) is clustering. The analyzed approaches for clustering incomplete data are: preprocessing (marginalization and imputation) and specialized clustering algorithms (PDS, IFCM, OCS, NPS). The objective of our research is the comparison of the preprocessing techniques and specialized clustering algorithms to find the the most-advantageous technique for handling incomplete data with a neuro-fuzzy system. This approach is also the indirect validation of clustering

Rough Fuzzy Subspace Clustering for Data with Missing Values

The paper presents rough fuzzy subspace clustering algorithm and experimental results of clustering. In this algorithm three approaches for handling missing values are used: marginalisation, imputation and rough sets. The algorithm also assigns weights to attributes in each cluster; this leads to subspace clustering. The parameters of clusters are elaborated in the iterative procedure based on minimising of criterion function. The crucial parameter of the proposed algorithm is the parameter having the influence on the sharpness of elaborated subspace cluster. The lower values of the parameter lead to selection of the most important attribute. The higher values create clusters in the global space, not in subspaces. The paper is accompanied by results of clustering of synthetic and real life data sets