Abstract The priori knowledge of the radar can not be used by the traditional fuzzy C-means clustering algorithm, which leads a poor accuracy of the data association. An improved fuzzy C-means clustering algorithm is proposed in this paper. The real-time change rate of the track slope of moving targets measured by radar is used to update the weight. Then the objective function of fuzzy C-means ...

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

We present a new clustering algorithm called k-means-u* which in many cases is able to significantly improve the clusterings found by k-means++, the current de-facto standard for clustering in Euclidean spaces. First we introduce the k-means-u algorithm which starts from a result of k-means++ and attempts to improve it with a sequence of non-local “jumps” alternated by runs of standard k-means....

The k-means++ seeding algorithm is one of the most popular algorithms that is used for finding the initial k centers when using the k-means heuristic. The algorithm is a simple sampling procedure and can be described as follows: Pick the first center randomly from the given points. For i > 1, pick a point to be the i center with probability proportional to the square of the Euclidean distance o...

This paper proposed a new application of K-means clustering algorithm. Due to ease of implementation and application, K-means algorithm can be widely used. However, one of the disadvantages of clustering algorithms is that there is no balance between the clustering algorithm and its applications, and many researchers have paid less attention to clustering algorithm applications. The purpose of ...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

K-means algorithm is a relatively simple and fast gather clustering algorithm. However, the initial clustering center of the traditional k-means algorithm was generated randomly from the dataset, and the clustering result was unstable. In this paper, we propose a novel method to optimize the selection of initial centroids for k-means algorithm based on the small world network. This paper firstl...