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Comparison of Algorithms for Clustering Incomplete Data

Matyja A, Siminski K

Foundations of Computing and Decision Sciences

2014

Vol. 39, No. 2

107--127

The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

Neuro-rough-fuzzy approach for regression modelling from missing data

Simiński K.

International Journal of Applied Mathematics and Computer Science

2012

Vol. 22, no. 2

461-476

Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.