王树良
个性化签名
- 姓名:王树良
- 目前身份:
- 担任导师情况:
- 学位:
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学术头衔:
教育部“新世纪优秀人才支持计划”入选者, 博士生导师
- 职称:-
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学科领域:
计算机软件
- 研究兴趣:
王树良,男,教授,博士生导师。Data Mining and Knowledge Discovery、ISPRS Journal of Photogrammetry and Remote Sensing等期刊审稿人。主要研究空间数据挖掘等。
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主页访问
6937
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关注数
0
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成果阅读
764
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成果数
9
【期刊论文】HGCUDF: Hierarchical Grid Clustering Using Data Field
王树良, WANG Shuliang, FAN Jinghua, FANG Meng, YUAN Hanning
Chinese Journal of Electronics,-0001,():
-1年11月30日
A new clustering algorithm of Hierarchical grid clustering using data field (HGCUDF) is proposed. Under the distributed characteristics of data points on objects, the hierarchical grids divide and conquer the large datasets in their hierarchical subsets, which reduces the scope in search of the clustering centers, and minifies the area of data space for generating data field. The compared experiments show that HGCUDF computes the grids rather than retrieves all data from database, for improving the efficiency.
Hierarchical grid clustering using data field (, HGCUDF), ,, Data field,, Spatial clustering.,
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101浏览
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17下载
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【期刊论文】Community detection in complex networks by density-based clustering
王树良, Hong Jin, Shuliang Wang, Chenyang Li
Physica A,-0001,():
-1年11月30日
We proposed a method to find the community structure in a complex network by densitybased clustering. Physical topological distance is introduced in density-based clustering for determining a distance function of specific influence functions. According to the distribution of the data, the community structures are uncovered. The method keeps a better connection mode of the community structure than the existing algorithms in terms of modularity, which can be viewed as a basic characteristic of community detection in the future. Moreover, experimental results indicate that the proposed method is efficient and effective to be used for community detection of medium and large networks.
Physical topological distance,, Density-based clustering,, Community detection
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151浏览
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23下载
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【期刊论文】Data Field for Hierarchical Clustering
王树良, Shuliang Wang, Wenyan Gan, Deyi Li, Deren Li
International Journal of Data Warehousing and Mining,-0001,():
-1年11月30日
In this paper, data field is proposed to group data objects via simulating their mutual interactions and opposite movements for hierarchical clustering. Enlightened by the field in physical space, data field to simulate nuclear field is presented to illuminate the interaction between objects in data space. In the data field, the self-organized process of equipotential lines on many data objects discovers their hierarchical clustering-characteristics. During the clustering process, a random sample is first generated to optimize the impact factor. The masses of data objects are then estimated to select core data object with nonzero masses. Taking the core data objects as the initial clusters, the clusters are iteratively merged hierarchy by hierarchy with good performance. The results of a case study show that the data field is capable of hierarchical clustering on objects varying size, shape or granularity without user-specified parameters, as well as considering the object features inside the clusters and removing the outliers from noisy data. The comparisons illustrate that the data field clustering performs better than K-means, BIRCH, CURE, and CHAMELEON.
Core Objects,, Data Field,, Data Mining,, Hierarchical Clustering,, Interaction Among Objects
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154浏览
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189下载
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王树良
,-0001,():
-1年11月30日
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87浏览
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305下载
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50浏览
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155下载
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51浏览
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122下载
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57浏览
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155下载
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47浏览
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311下载
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66浏览
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60下载
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