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  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls
  • Statistics In Transition



Improved K-means Algorithm Based on optimizing Initial Cluster Centers and Its Application

Data mining is a process of data grouping or partitioning from the large and complex data, and the clustering analysis is an important research field in data mining. The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. By constantly iterating, the distance between each data object and the center of its subclass is minimized. Because K-means

Xue Linyao, Wang Jianguo

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 9–16


Application of Wireless Return Topology Planning Based on K-Means

, the maximum access number of sub-stations is 12, covering a wider range, so butterfly station should be selected as the host station as far as possible. In real life, the deployment of the site is usually partitions, according to the “division”, “local optimum, and the thought of” step by step optimization”, first by using K Means clustering algorithm, all the site is divided into K classes, each kind of belonging to a satellite, then each category divided into n host station tribal groups, the

Weixia Yang, He Li, Yuan Chen, Fei Xu

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 61–68


Application of K-means Algorithm in Geological Disaster Monitoring System

well. K-means algorithm is a clustering algorithm based on the classification of the classic algorithm, the algorithm in the industrial and commercial applications more widely. As we all know, it both has many advantages and many disadvantages. In this paper, we mainly study the optimization of the initial clustering center and the avoidance of the blindness of the k-value selection, and propose the CMU-kmeans algorithm. The data source of the study is the historical data detected by the geological

Wang Jianguo, Xue Linyao

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 16–22


Design and Implementation of Music Recommendation System Based on Hadoop

, database and ETL operation, which can calculate a set of complete recommendation system from user operation end to server and data calculation. In order to improve the accuracy of the recommendation algorithm, this paper introduces k-means clustering algorithm to improve the recommendation algorithm based on user-based collaborative filtering.The experimental results show that the accuracy of the proposed algorithm has been significantly improved after the introduction of k-means.

Zhao Yufeng, Li Xinwei

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 126–132


Hazard Grading Model of Terrorist Attack Based on Machine Learning

field includes the number of all victims and terrorists who directly caused death from terrorist incidents. We use only requires the number of victims and does not require the death toll of terrorists. Therefore, the number of victims is obtained by subtracting the number of terrorist deaths (nkiller) from the total number of deaths. III. TERRORIST ATTACK HAZARD CLASSIFICATION MODEL In this paper, the PCA algorithm, K-means clustering algorithm and entropy method are used to classify the

Jun Yu, Tong Xian, Zhiyi Hu, Yutong Liu

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 81–85



wrapper methods. The new method of variable selection returns best results when the classical k-means method of objects grouping is slightly modified.

Jerzy Korzeniewski

Statistics in Transition New Series , ISSUE 2, 295–304



: cluster analysis, k-means method, generalised distance measure GDM and interval taxonomic method TMI. The analysis was performed on the basis of HETUS data.

Marta Hozer-Koćmiel, Christian Lis

Statistics in Transition New Series , ISSUE 2, 317–330


Improved Faster R-CNN Algorithm for Sea Object Detection Under Complex Sea Conditions

detection accuracy of the above method is not high. In this paper, the Faster R-CNN[11-13] object detection algorithm is improved. The K-Means clustering algorithm[14] is introduced to perform cluster analysis on the size of the object in the image, and the clustering results are directly input into the area recommendation network to achieve the improvement of the area recommendation network. Using Soft -The NMS algorithm replaces the NMS algorithm to reduce the miss detection probability of small

Liu Yabin, Yu Jun, Hu Zhiyi

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 76–82


Analysis and Design of Image Segmentation Algorithm Based on Super-pixel and Graph Cut

Feng Xiao, Hao Sun

International Journal of Advanced Network, Monitoring and Controls , ISSUE 4, 25–30



confidence of the potential lane lines, and a K-means cluster algorithm is employed to localize the lane lines; Finally, a on board system is designed for experiment. The proposed system is shown to work well under various conditions on the roadway. Besides, the computation cost is inexpensive and the system's response is almost real time.

Xiaodong Miao, Shunming Li, Huan Shen

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 957–972

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