Review on Fuzzy Clustering Algorithms

Image segmentation especially fuzzy-based segmentation techniques are widely used due to effective segmentation performance. For this reason, a number of algorithms are proposed in the literature. This paper presents a survey report of different types of classical fuzzy clustering techniques which are available in the literature

A robust clustering procedure for fuzzy data

AbstractIn this paper we propose a robust clustering method for handling LR-type fuzzy numbers. The proposed method based on similarity measures is not necessary to specify the cluster number and initials. Several numerical examples demonstrate the effectiveness of the proposed robust clustering method, especially robust to outliers, different cluster shapes and initial guess. We then apply this algorithm to three real data sets. These are Taiwanese tea, student data and patient blood pressure data sets. Because tea evaluation comes under an expert subjective judgment for Taiwanese tea, the quality levels are ambiguity and imprecision inherent to human perception. Thus, LR-type fuzzy numbers are used to describe these quality levels. The proposed robust clustering method successfully establishes a performance evaluation system to help consumers better understand and choose Taiwanese tea. Similarly, LR-type fuzzy numbers are also used to describe data types for student and patient blood pressure data. The proposed method actually presents good clustering results for these real data sets

A Novel Approach to Fuzzy Clustering based on a Dissimilarity Relation extracted from Data using a TS System

Clustering refers to the process of unsupervised partitioning of a data set based on a dissimilarity measure, which determines the cluster shape. Considering that cluster shapes may change from one cluster to another, it would be of the utmost importance to extract the dissimilarity measure directly from the data by means of a data model. On the other hand, a model construction requires some kind of supervision of the data structure, which is exactly what we look for during clustering. So, the lower the supervision degree used to build the data model, the more it makes sense to resort to a data model for clustering purposes. Conscious of this, we propose to exploit very few pairs of patterns with known dissimilarity to build a TS system which models the dissimilarity relation. Among other things, the rules of the TS system provide an intuitive description of the dissimilarity relation itself. Then we use the TS system to build a dissimilarity matrix which is fed as input to an unsupervised fuzzy relational clustering algorithm, denoted any relation clustering algorithm (ARCA), which partitions the data set based on the proximity of the vectors containing the dissimilarity values between each pattern and all the other patterns in the data set. We show that combining the TS system and the ARCA algorithm allows us to achieve high classification performance on a synthetic data set and on two real data sets. Further, we discuss how the rules of the TS system represent a sort of linguistic description of the dissimilarity relation

Метод вбудовування даних у зображення за можливості JPEG–стиснення

Запропоновано еволюційний підхід підвищення ефективності шаблонної схеми вбудовування даних зa можливості JPEG-стиснення, що полягає в ітеративному покращанні складу множини стеганографічно використовуваних шаблонів.Suggests an evolutional approach of efficiency increasing of data embedding template technique by possibility of jpeg-compression, which consists in iterative improvement of templates set contents for steganographic use

Метод вбудовування даних у зображення за можливості JPEG–стиснення

Запропоновано еволюційний підхід підвищення ефективності шаблонної схеми вбудовування даних зa можливості JPEG-стиснення, що полягає в ітеративному покращанні складу множини стеганографічно використовуваних шаблонів.Suggests an evolutional approach of efficiency increasing of data embedding template technique by possibility of jpeg-compression, which consists in iterative improvement of templates set contents for steganographic use