Papers by Keyword: K-Means Method

Abstract: In this paper, to achieve waste heat utilization of large-scale comprehensive public study of Shenyang part of large-scale comprehensive hospital, first it has carried on the statistics of hospital waste heat utilization of point, adopting the K - means methods to classify the hospital waste heat utilization point and analyze the waste heat and establish waste heat utilization system .Then four paths of waste heat utilization have been got to get a further hospital waste heat utilization system of waste heat recovery efficiency, optimization study for the future.

Abstract: Load model has a great impact on the digital simulation result. In this paper, the measurement-based method is applied to model the load. If all the measured data are used for modeling respectively, the workload would be increased greatly. But if only one model is generated with the multi-curve fitting parameter identification method, the accuracy of modeling would be reduced greatly. The clustering analysis theory supplies an effective way to solve the problem above. There are some methods for clustering introduced in this paper. But a suitable method needs be studied firstly. The case study is presented to compare these methods. According to simulation result, it is concluded that the Kmeans method is best, while the usually adopted central clustering is actually not suitable for the load time-variation research.

Abstract: A kind of atypical unexpected incidents hide in complaint text accompany with the telecom services. This atypical unexpected incident is defined as AUI. AUI has some special attributes as high-cohesion and space-sparse. To process the data with ordinary K-means method, the most essential thing is to find the K clustering centers accurately. Anyway, it is not guaranteed in ordinary K-means method. This work proposes an optimization using genetic algorithm. We design a fitness function, and find out the global optimal K centers. The experiment shows the most accurate clustering result.

Abstract: The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).