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
International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 9–16
, 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
International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 61–68
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
International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 16–22
, 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.
International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 126–132
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.
TERRORIST ATTACK HAZARD CLASSIFICATION MODEL
In this paper, the PCA algorithm, K-means clustering algorithm and entropy method are used to classify the
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.
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.
Statistics in Transition New Series , ISSUE 2, 317–330
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 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
International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 76–82
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.
International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 957–972