Predicting Yeast Protein Localization Sites by a New Clustering Algorithm Based on Weighted Feature Ensemble

Fan, Jiancong; Feng, Zheng; Liu, Wenhua; Pang, Jinrong; Liang, Yongquan

doi:10.1166/jctn.2014.3682

Cited by 3 publications

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“…The bottom-up clustering method is an inductive process and more natural than the top-down, so the bottomup clustering strategy is more widely applied [2,11]. In addition to the above two kinds of clustering methods, probability-based clustering method has caused the great concern of many researchers in recent years [12][13][14][15][16][17][18][19][20][21]. Probability-based clustering method mainly includes model-based strategy [12][13][14][15][16] and evolutionary probabilistic modeling strategy [17][18][19][20][21].…”

Section: Related Workmentioning

confidence: 99%

OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

Fan

2015

Neural Comput & Applic

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The Survival of the Fittest is a principle which selects the superior and eliminates the inferior in the nature. This principle has been used in many fields, especially in optimization problem-solving. Clustering in data mining community endeavors to discover unknown representations or patterns hidden in datasets. Hierarchical clustering algorithm (HCA) is a method of cluster analysis which searches the optimal distribution of clusters by a hierarchical structure. Strategies for hierarchical clustering generally have two types: agglomerative with a bottom-up procedure and divisive with a top-down procedure. However, most of the clustering approaches have two disadvantages: the use of distance-based measurement and the difficulty of the clusters integration. In this paper, we propose an optimal probabilistic estimation (OPE) approach by exploiting the Survival of the Fittest principle. We devise a hierarchical clustering algorithm (HCA) based on OPE, also called OPE-HCA. The OPE-HCA combines optimization with probability and agglomerative HCA. Experimental results show that the OPE-HCA has the ability of searching and discovering patterns at different description levels and can also obtain better performance than many clustering algorithms according to NMI and clustering accuracy measures.

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