A New Kind of Based on the Graph K-Means Clustering Initial Center Selection Algorithm

2012 ◽  
Vol 241-244 ◽  
pp. 2845-2848 ◽  
Author(s):  
Hai Yan Zhou
Keyword(s):  
Image Processing ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Simple Algorithm ◽  
Geometric Meaning ◽  
Direct Impact ◽  
Selection Algorithm ◽  
Initial Cluster ◽  
Cluster Class ◽  
Better Than

K-means clustering algorithm is simple and fast, and has more intuitive geometric meaning, which has been widely applied in pattern recognition, image processing and computer vision. It has obtained satisfactory results. But it need to determine the initial cluster class center before executing the k-means algorithm, and the choice of the initial cluster class center has a direct impact on the final clustering results. A selection algorithm is proposed, which based on figure node most magnanimous to determine the initial cluster class center of K-means clustering algorithm. The method compares with the selection algorithm of other initial cluster class center, which has a simple algorithm idea and low time complexity, and it is significantly better than other clustering arithmetic.

scholarly journals Adaptive Initialization Method for K-Means Algorithm

2021 ◽  
Vol 4 ◽  
Author(s):  
Jie Yang ◽  
Yu-Kai Wang ◽  
Xin Yao ◽  
Chin-Teng Lin
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Superior Performance ◽  
Local Optima ◽  
Initial Cluster ◽  
Higher Dimensional ◽  
Real World Datasets ◽  
Random Method

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.

Quantum image interest point extraction

2021 ◽  
pp. 2150063
Author(s):  
Nan Jiang ◽  
Zhuoxiao Ji ◽  
Hong Li ◽  
Jian Wang
Keyword(s):  
Image Processing ◽  
Time Complexity ◽  
Luminance Contrast ◽  
Target Object ◽  
Interest Point ◽  
Interest Points ◽  
Quantum Image ◽  
Absolute Value ◽  
Classical Image ◽  
Better Than

With the development of quantum computing, the application of it to image processing has lots of advantages compared to classical image processing. In this paper, we propose a scheme to extract the interest point in quantum images. Interest point is a kind of feature point which can help to identify the target object in the image. Our scheme is based on the idea of Luminance Contrast (LC) algorithm. The scheme computes the absolute value of gray level differences between a pixel and the others, and then adds all these differences together. The sum is defined as a saliency. After computing the saliency of every pixel, we label the pixels with the maximal saliency as the interest points. The algorithm has pretty good performance and its time complexity is much better than the classical algorithm in same conditions, which provides a new idea for the extraction of image interest point.

A Geometry-Based Accelerated Fusion Clustering Algorithm and its Application in Marine Engineering

2010 ◽  
Vol 108-111 ◽  
pp. 106-111 ◽  
Author(s):  
Tian Zhen Wang ◽  
Yang Liu ◽  
Tian Hao Tang
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Marine Engineering ◽  
Selection Of ◽  
Better Than

In order to solve the problem in k-means algorithm that inappropriate selection of initial clustering centers often causes clustering in local optimum and the time complexity is too high when handling large amounts of data, a fusion clustering algorithm based on geometry is proposed in this paper. The result of experiments shows this algorithm is better than the traditional k-means and the k-means++ algorithms, with higher quality and faster speed. And at last in this paper, we apply it in marine engineering.

scholarly journals IKM-NCS: A Novel Clustering Scheme Based on Improved K-Means Algorithm

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Clustering Algorithm ◽  
User Behavior ◽  
Abnormal Behavior ◽  
Cluster Center ◽  
Initial Sample ◽  
Initial Cluster ◽  
Weight Calculation ◽  
Data Points ◽  
Abnormal Behaviors ◽  
Better Than

Aiming at the problems of distorted center selection and slow iteration convergence in traditional clustering analysis algorithm, a novel clustering scheme based on improved k-means algorithm is proposed. In this paper, based on the analysis of all user behavior sets contained in the initial sample, a weight calculation method for abnormal behaviors and an eigenvalue extraction method for abnormal behavior set are proposed and a set of abnormal behaviors is constructed for each user according to the behavior data generated by abnormal users. Then, on the basis of the traditional k-means clustering algorithm, an improved algorithm is proposed. By calculating the compactness of all data points and selecting the initial cluster center among the data points with high and low compactness, the clustering performance is enhanced. Finally, the eigenvalues of the abnormal behavior set are used as the input of the algorithm to output the clustering results of the abnormal behavior. Experimental results show that the clustering performance of this algorithm is better than the traditional clustering algorithm, and can effectively improve the clustering performance of abnormal behavior

IMAGE PROCESSING VIA CNN GENES WITH VON NEUMANN NEIGHBORHOODS

2005 ◽  
Vol 15 (12) ◽  
pp. 3999-4006 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
FANG-YUE CHEN ◽  
GUO-LONG HE
Keyword(s):  
Image Processing ◽  
Computer Simulation ◽  
Edge Detection ◽  
Vertical Line ◽  
Corner Detection ◽  
Line Detection ◽  
Von Neumann ◽  
Center Point ◽  
Better Than

Some image processing research are restudied via CNN genes with five variables, and this include edge detection, corner detection, center point extraction and horizontal-vertical line detection. Although they were implemented with nine variables, the results of computer simulation show that the effect with five variables is identical to or better than that with nine variables.

scholarly journals Cryptanalysis of Loiss Stream Cipher-Revisited

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Lin Ding ◽  
Chenhui Jin ◽  
Jie Guan ◽  
Qiuyan Wang
Keyword(s):  
Time Complexity ◽  
Stream Cipher ◽  
Linear Equations ◽  
Data Complexity ◽  
Secret Key ◽  
Key Recovery ◽  
Systems Of Linear Equations ◽  
Key Recovery Attack ◽  
Better Than

Loiss is a novel byte-oriented stream cipher proposed in 2011. In this paper, based on solving systems of linear equations, we propose an improved Guess and Determine attack on Loiss with a time complexity of 2231and a data complexity of 268, which reduces the time complexity of the Guess and Determine attack proposed by the designers by a factor of 216. Furthermore, a related key chosenIVattack on a scaled-down version of Loiss is presented. The attack recovers the 128-bit secret key of the scaled-down Loiss with a time complexity of 280, requiring 264chosenIVs. The related key attack is minimal in the sense that it only requires one related key. The result shows that our key recovery attack on the scaled-down Loiss is much better than an exhaustive key search in the related key setting.

An improved OPTICS clustering algorithm for discovering clusters with uneven densities

2021 ◽  
Vol 25 (6) ◽  
pp. 1453-1471
Author(s):  
Chunhua Tang ◽  
Han Wang ◽  
Zhiwen Wang ◽  
Xiangkun Zeng ◽  
Huaran Yan ◽  
...  
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Substantial Improvement ◽  
Experimental Results ◽  
High Time ◽  
Parameter Setting ◽  
K Nearest Neighbor ◽  
Density Based Clustering

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

Measuring and Monitoring Urban Sprawl of Jaipur City using Remote Sensing and GIS

2015 ◽  
Vol 6 (2) ◽  
pp. 46-65
Author(s):  
Pushpendra Singh Sisodia ◽  
Vivekanand Tiwari ◽  
Anil Kumar Dahiya
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Sustainable Development ◽  
Developing Countries ◽  
Maximum Likelihood ◽  
Natural Resources ◽  
Urban Sprawl ◽  
Remote Sensing And Gis ◽  
Direct Impact ◽  
Maximum Likelihood Classification

The world's population increased drastically and forced people to migrate from rural area to major cities in search of basic amenities. The majority of the World's population are already living in the major cities and it is continuously increasing. The increase in population forced the major cities to expand. Expansion of cities acclaimed more unplanned settlement that leads unplanned growth. This is a global phenomenon that has a direct impact on natural resources. It is the biggest challenge for urban planners to achieve sustainable development. Developing countries like India, where the population is increasing at an alarming pace, require more attention towards this problem. In this study, an attempt has been made to measure and monitor urban sprawl in Jaipur (Capital, State of Rajasthan, India). Built-up area with corresponding population has been analysed over a period of 41 years (1972-2013). Remotely sensed images of 1972-2013 (MSS, TM and ETM+) have been classified using Supervised Maximum Likelihood Classification (MLC) for digital image processing. Shannon's entropy has been used to quantify the degree of urban sprawl, and eight landscape metrics have also been used to quantify urban sprawl and its pattern.

scholarly journals Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture

2019 ◽  
Vol 9 (2S) ◽  
pp. 9-13
Keyword(s):  
Cluster Analysis ◽  
Precision Agriculture ◽  
Crop Production ◽  
Clustering Algorithm ◽  
Research Work ◽  
K Value ◽  
Clustering Technique ◽  
Production Analysis ◽  
Initial Cluster ◽  
The Stability

The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

scholarly journals Image Hiding Using QR Factorization And Discrete Wavelet Transform Techniques

2021 ◽  
Vol 6 (2) ◽  
pp. 72-81
Author(s):  
Reham Ahmed El-Shahed ◽  
◽  
Maryam Al-Berry ◽  
Hala Ebied ◽  
Howida A. Shedeed ◽  
...  
Keyword(s):  
Data Security ◽  
Color Image ◽  
Simple Algorithm ◽  
Qr Factorization ◽  
Discrete Wavelet ◽  
Huge Amount ◽  
Image Hiding ◽  
Media Applications ◽  
Very High ◽  
Better Than

Steganography is one of the most important tools in the data security field as there is a huge amount of data transferred each moment over the internet. Hiding secret messages in an image has been widely used because the images are mostly used in social media applications. The proposed algorithm is a simple algorithm for hiding an image in another image. The proposed technique uses QR factorization to conceal the secret image. The technique successfully hid a gray and color image in another one and the performance of the algorithm was measured by PSNR, SSIM and NCC. The PSNR for the cover image was in the range of 41 to 51 dB. DWT was added to increase the security of the method and this enhanced technique increased the cover PSNR to 48 t0 56 dB. The SSIM is 100% and the NCC is 1 for both implementations. Which improves that the imperceptibility of the algorithm is very high. The comparative analysis showed that the performance of the algorithm is better than other state-of-the-art algorithms

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