Purpose The primary aim of this study is to review the studies from different dimensions
including type of methods, experimentation setup and evaluation metrics used in the novel …

Missing values are a common phenomenon when dealing with real-world data sets.
Analysis of incomplete data sets has become an active area of research. In this paper, we …

In the clustering of incomplete data, the processing of missing attribute values and the
optimization procedure of clustering are always of concern. In this paper, a novel clustering …

This paper presents a method for utilizing Data Envelopment Analysis (DEA) with sparse
input and output data using fuzzy clustering concepts. DEA, a methodology to assess …

Partially missing data sets are a prevailing problem in clustering analysis. We propose a
hybrid algorithm combining fuzzy clustering with particle swarm optimization (PSO) for …

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 …

Incomplete data clustering is often encountered in practice. Here the treatment of missing
attribute value and the optimization procedure of clustering are the important factors …

The missing values can be an important obstacle and challenging problem in data analysis.
The paper presents the neuro-fuzzy system that handles incomplete data. The system is …

The paper presents the clustering algorithm for data with missing values. In this approach
both marginalisation and imputation are applied. The result of the clustering is the type-2 …

Support vector regression-based fuzzy c-means algorithm (SVR-FCM) clusters data
according to their relationship among attributes, which can provide competitive clustering …