Novel Two-Dimensional Visualization Approaches for Multivariate Centroids of Clustering Algorithms

Two-Dimensional Clustering Algorithms for Image Segmentation

This paper introduces modified versions of the K-Means (KM) and Moving K-Means (MKM) clustering algorithms, called the Two-Dimensional K-Means (2D-KM) and Two-Dimensional Moving KMeans (2D-MKM) algorithms respectively. The performances of these two proposed algorithms are compared with three of the commonly used conventional clustering algorithms, namely K-Means (KM), Fuzzy C-Means (FCM), and M...

Novel Graph Based Clustering and Visualization Algorithms for Data Mining

Signal processing approaches as novel tools for the clustering of N-acetyl-β-D-glucosaminidases

Nowadays, the clustering of proteins and enzymes in particular, are one of the most popular topics in bioinformatics. Increasing number of chitinase genes from different organisms and their sequences have beenidentified. So far, various mathematical algorithms for the clustering of chitinase genes have been used butmost of them seem to be confusing and sometimes insufficient. In the...

Two Novel Chaos-Based Algorithms for Image and Video Watermarking

In this paper we introduce two innovative image and video watermarking algorithms. The paper’s main emphasis is on the use of chaotic maps to boost the algorithms’ security and resistance against attacks. By encrypting the watermark information in a one dimensional chaotic map, we make the extraction of watermark for potential attackers very hard. In another approach, we select embedding po...

Comparison Of Modern Clustering Algorithms For Two-Dimensional Data

Cluster analysis or clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining and a common technique for statistical data analysis used in many fields, including machine learning, pattern recogn...