Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Fast principal component analysis using fixed-point algorithm
Pattern Recognition Letters
Unsupervised data pruning for clustering of noisy data
Knowledge-Based Systems
Classifying genes according to predefined patterns by controlling false discovery rate
Expert Systems with Applications: An International Journal
Finding key attribute subset in dataset for outlier detection
Knowledge-Based Systems
A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval
Knowledge-Based Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Cluster analysis is an unsupervised learning technique of partitioning objects into several homogeneous groups. If noises are included in the data set, they should be eliminated in the course of clustering. Although soft clustering methods can handle noise-included data, most of them do not give an appropriate guideline for discriminating noise objects from significant objects. We propose a multiple testing procedure to filter out noises and cluster significant objects while simultaneously maintaining the decision error within the target level. To handle high-dimensional data, we reduce the dimension of attributes by using the principal component analysis and model the objects using the Gaussian mixture. The proposed two-phase procedure is effective in noise separation and in estimation of Gaussian mixture. We applied the proposed procedure to two real and two synthetic data sets. Experimental results show that the proposed method works effectively for high-dimensional data.