Clustering algorithms aim at finding dense regions of data based on similarities and
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …

The study of brain disorders requires accurate tissue segmentation of magnetic resonance
(MR) brain images which is very important for detecting tumors, edema, and necrotic tissues …

The two most widely studied clustering algorithms are partitional and hierarchical clustering.
These algorithms have been heavily used in a wide range of applications primarily due to …

Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …

Clustering is one of the most widely used approaches in data mining with real life
applications in virtually any domain. The huge interest in clustering has led to a possibly …

To achieve more accurate identification of mechanical faults for high voltage circuit breaker
(HVCB) with higher speed, multi-sensor information fusion has been proposed in this paper …

Intuitionistic fuzzy sets and rough sets are widely used for medical image segmentation, and
recently combined together to deal with uncertainty and vagueness in medical images. In …

Automated brain tumor segmentation of MR image is a very challenging task in a medical
point of view. As the nature of the tumor, it can appear anywhere in the brain region with any …

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical
image segmentation, and have recently been combined together to better deal with the …

Fuzzy time series forecasting is an emergent research topic. In fuzzy time series model
design, accuracy of forecast is dependent on two major issues:(1) Efficient data partitioning …