In this study, we would like to present brain tumor detection methods, based on the conventional K-means technique, Expectation Maximization (EM) algorithm and a new Spatial Fuzzy-technique analysis of brain MR images. Though, the Kmeans and EM algorithm were already used in Brain MR image segmentation, as well as image segmentation in general, it fails to utilize the strong spatial correlation...

The partition-based clustering algorithms, like KMeans and fuzzy K-Means, are most widely and successfully used in data mining in the past decades. In this paper, we present a robust and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means algorithm by incorporating a robust function, rather than the square data fitting term, to handle outliers. More importantly...

Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted near...

This paper proposes a semi-supervised learning method using Fuzzy clustering to solve word sense disambiguation problems. Furthermore, we reduce side effects of semi-supervised learning by ensemble learning. We set classes for labeled instances. The -th labeled instance is used as the prototype of the -th class. By using Fuzzy clustering for unlabeled instances, prototypes are moved to more sui...

An automated surveillance system should have the ability to recognize human behaviour and to warn security personnel of any impending suspicious activity. Human posture is one of the key aspects of analyzing human behaviour. We investigated three clustering techniques to recognize human posture. The system is first trained to recognize a pair of posture and this is repeated for three pairs of h...

Because of the massive increase in the size of the data it becomes troublesome to perform effective analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of dat...

Zusammenfassung. Eine neue Methode wird vorgestellt, die es erlaubt unscharfes Vorwissen  uber Objektkonturen in ein Modell Aktiver Konturen ("Snakes") zu integrieren. Das neue Konzept der Fuzzy Snakes wurde entwickelt, um die Eigenschaften einer Objektkontur in intuitiver Weise beschreiben zu k onnen. Zu diesen Eigenschaften zahlen neben der durch das bildgebende Verfahren bestimmten Ersche...

In this paper, classification efficiency of the conventional K-nearest neighbor algorithm is enhanced by exploiting fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbor algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the gener...

For a non-degenerate convex subset Y of the n-dimensional Euclidean space Rn , let K(Y ) be the family of all fuzzy sets of Rn , which are upper-semicontinuous, fuzzy convex and normal with compact supports contained in Y. We show that the space K(Y ) with the topology of endograph metric is homeomorphic to the Hilbert cube Q = [−1, 1] iff Y is compact; and the space K(Y ) is homeomorphic to {(...