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Clustering analysis is a primary method for data mining. Density clustering has such advantages as: its clusters are easy to understand and it does not limit itself to shapes of clusters. But existing density-based algorithms have trouble in finding out all the meaningful clusters for datasets with varied densities. This paper introduces a new algorithm called VDBSCAN for the purpose of varied-density...
Clustering is one of the most heated topics in data mining research. In traditional clustering algorithms, each feature is treated equally and each one does the same contribution to clustering. As a matter of fact, redundant and unrelated features may disturb the clustering result. This paper proposed a fuzzy feature selection strategy to improve the clustering algorithm. The strategy is based on...
Data mining approaches have been widely applied in the field of healthcare. At the same time it is recognized that most healthcare datasets are full of missing values. In this paper we apply decision trees, Naive Bayesian classifiers and feature selection methods to a geriatric hospital dataset in order to predict inpatient length of stay, especially for the long stay patients
In data mining approaches, predictive classification has a wide range of application. However, there are always missing data in the datasets, which affect the accuracy of classifiers. This paper investigates the influence of missing data to classifier. The sensitivity analysis of six classifiers to missing data is studied in experiments. The results indicate that, in the datasets, when the proportion...
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