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王树良
,-0001,():
-1年11月30日
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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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