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Data Field for Hierarchical Clustering

Data Field for Hierarchical Clustering

Shuliang Wang, Wenyan Gan, Deyi Li, Deren Li
Copyright: © 2013 |Pages: 22
ISBN13: 9781466621480|ISBN10: 1466621486|EISBN13: 9781466621497
DOI: 10.4018/978-1-4666-2148-0.ch014
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MLA

Wang, Shuliang, et al. "Data Field for Hierarchical Clustering." Developments in Data Extraction, Management, and Analysis, edited by Nhung Do, et al., IGI Global, 2013, pp. 303-324. https://doi.org/10.4018/978-1-4666-2148-0.ch014

APA

Wang, S., Gan, W., Li, D., & Li, D. (2013). Data Field for Hierarchical Clustering. In N. Do, J. Rahayu, & T. Torabi (Eds.), Developments in Data Extraction, Management, and Analysis (pp. 303-324). IGI Global. https://doi.org/10.4018/978-1-4666-2148-0.ch014

Chicago

Wang, Shuliang, et al. "Data Field for Hierarchical Clustering." In Developments in Data Extraction, Management, and Analysis, edited by Nhung Do, J. Wenny Rahayu, and Torab Torabi, 303-324. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2148-0.ch014

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Abstract

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.

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