Existing clustering algorithms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on predefined parameters specified by users. These algorithms may be unsuccessful if the choic...
详细信息
Existing clustering algorithms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on predefined parameters specified by users. These algorithms may be unsuccessful if the choice of parameters is inappropriate with respect to the data set being clustered. Most of these algorithms work very well for compact and hyper-spherical clusters. In this paper, a new hybrid clustering algorithm called Self-Partition and Self-Merging (SPSM) is proposed. The SPSM algorithm partitions the input data set into several subclusters in the first phase and, then, removes the noisy data in the second phase. In the third phase, the normal subclusters are continuously merged to form the larger clusters based on the inter-cluster distance and intra-cluster distance criteria. From the experimental results, the SPSM algorithm is very efficient to handle the noisy data set, and to cluster the data sets of arbitrary shapes of different density. Several examples for color image show the versatility of the proposed method and compare with results described in the literature for the same images. The computational complexity of the SPSM algorithm is O(N-2), where N is the number of data points.
Existing clustering algorithtms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on pre-defined parameters specified by users. These algorithms may be un-successful if the ch...
详细信息
ISBN:
(纸本)9783642013065
Existing clustering algorithtms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on pre-defined parameters specified by users. These algorithms may be un-successful if the choice of parameters is inappropriate with respect to the data set being clustered. Most of these algorithms work very well for compact and hyperspherical clusters. In this paper, a new hybrid clustering algorithm called Self-Partition, and, Self-Merging (SPSM) is proposed. The SPSM algorithm partitions the input data set into several subclusters in the first phase and, then, removes the noisy data in the second phase. In the third phase, the normal subclusters are continuously merged to form the larger clusters based on the inter-cluster distance and intra-cluster distance criteria. From the experimental results, the SPSM algorithm is very efficient to handle the noisy data set;and to cluster the data. sets of arbitrary shapes of different density.
Researchers at UCSD, with the initial support of NSF and the current support of the Federal Railroad Administration (FRA), have been working on a flaw detection prototype for rails that uses non-contact ultrasonic pro...
详细信息
ISBN:
(纸本)9780819466501
Researchers at UCSD, with the initial support of NSF and the current support of the Federal Railroad Administration (FRA), have been working on a flaw detection prototype for rails that uses non-contact ultrasonic probing and robust data processing algorithms to provide high speed and high reliability defect detection in these structures. Besides the obvious advantages of non-contact probing, the prototype uses ultrasonic guided waves able to detect and quantify transverse cracks in the rail head, notoriously the most dangerous of all rail track defects. This paper will report on the first field test which was conducted in Gettysburg, PA in March 2006 with the technical support of ENSCO, Inc. Good results were obtained for the detection of both surface-breaking and internal cracks ranging in size from 2% cross-sectional head area (H.A.) reduction to 80% H.A. reduction.
Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in prac...
详细信息
Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.
Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. The field data used...
详细信息
Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. The field data used as input for training represented monthly changes in density and species richness in selected taxa of benthic macroinvertebrates collected in the Suyong River in Korea from September 1993 to October 1994. The sampled data for each month was initially trained by ART, the weights of which preserved conformational characteristics among communities during the process of the training. Subsequently these weights were rearranged sequentially from 2 to 5 months, and were provided as input to the Kohonen network to reveal temporal variations in communities. The network was then able to extract the features of community dynamics in a reduced dimension covering the specified input period. (C) 2000 Elsevier Science B.V. All rights reserved.
暂无评论