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Listing 50 manuscripts matching the search of "Clustering"


Study on cluster analysis characteristics and classification capabilities 2014 a case study of satisfaction regarding hotels and bed amp breakfasts of Chinese tourists in Taiwan

by Seng-Su Tsang, Wen-Cheng Wang, Hao-Hsiang Ku
Abstract

Cluster analysis is a multivariate statistical analysis method for the classification of samples based on the principle of 201Clike attracts like201D. It requires reasonable classification according to the characteristics in a reasonable manner, and without any mode for reference, in other words, classification is implemented without any prior knowledge. It has been applied in many aspects. In this paper, four cluster analysis methods are used to study the questionnaire data of Chinese tourists2019 satisfaction regarding Taiwan2019s hotels and Bed amp Breakfasts, (BampBs). First, this study applied principal component analysis in reducing questionnaire variables, and then gray relational analysis to assess the overall satisfaction performance. By sorting the overall satisfaction performance values, the performance values combined with the principle components were used as the testing sample data. Afterwards, the samples were categorized into three categories and four categories according to performance value. The four cluster analysis methods were used for clustering the principle components in order to observe their cluster performance and classification capabilities. The testing data testing results suggested that GK Cluster can obtain good cluster performance and good classification capabilities.

Volume: 23, Issue: 1

Recycling Plants Layout Design by Means of an Interactive Genetic Algorithm

by Laura Garcia-Hernandez, Antonio Arauzo-Azofra, Lorenzo Salas-Morera, Henri Pierreval, Emilio Corchado
Abstract

Facility Layout Design is known to be very important for attaining production efficiency because it directly influences manufacturing costs, lead times, work in process and productivity. Facility Layout problems have been addressed using several approaches. Unfortunately, these approaches only take into account quantitative criteria. However, there are qualitative preferences referred to the knowledge and experience of the designer, which should also be considered in facility layout design. These preferences can be subjective, not known in advance and changed during the design process, so that, it is difficult to include them using a classic optimization approach. For that reason, we propose the use of an Interactive Genetic Algorithm (IGA) for designing the layout of two real recycling plants taking into consideration subjective features from the designer. The designers knowledge guides the evolution of the algorithm evaluating facility layouts in each generation adjusting the search to his/her preferences. To avoid the fatigue of the designer, he/she evaluates only the most representative individuals of the population selected through a soft computing clustering method. The algorithm is applied on two real world waste recycling plant layout problems: a carton packs recycling plant and chopped plastic one. The results are compared with another method, proving that the new approach is able to capture the designer preferences in a reasonable number of iterations.

Volume: 19, Issue: 3

Cluster analysis of citrus genotypes using near-infrared spectroscopy

by Qiuhong Liao, Yanbo Huang, Shaolan He, Rangjin Xie, Qiang Lv, Shilai Yi, Yongqiang Zheng, Xi Tian, Lie Deng, Chun Qian
Abstract

There are many genotypes and varieties in the citrus family. Currently, citrus classification systems have significant divergences in varieties of species, and subgenus classification as well. In this study, near-infrared spectroscopy technique was used to acquire spectral information on the surface of citrus fruits. Cluster analysis was consequently conducted to identify citrus genotypes. Results indicated that the combination of 9-point moving average smoothing and multiplicative scattering correction was optimal for preprocessing spectral data. In the spectral range of 1,180–1,220nm, the cumulative reliability of the first two principal components were greater than 99.4%, and sweet oranges were clustered into an independent class. In 1,280–1,320nm, systematic clustering performed better than principal component clustering, and all other sour oranges, except Goutoucheng, were clustered into a single clade. With dimensions reduction, the cumulative reliability of first five principle components in full band of 1,000–2,350nm reached up to 99.1%. Using principal component cluster analysis, pomelo and loose-skin mandarin were clustered together; sweet and sour oranges were clearly separated. Pomelo being clustered with loose-skin mandarin, implies that they may have a hybrid origin; Jiaogan Mandarin, Daoxian yeju Mandarin, Goutoucheng sour oranges, and Zhuhongju sour tangerine were clustered with sweet orange, which implies old varieties may contain similar characteristic matters as sweet orange; Given that Jinlong lemon and Ranpour lime were clustered with sour orange, they were proved to originated from sour orange. The study indicates the great potential of spectral analysis for citrus genotype identification and classification.

Volume: 19, Issue: 3

Image Segmentation Method for Crop Nutrient Deficiency Based on Fuzzy C-Means Clustering Algorithm

by Jing Hu, Daoliang Li, Guifen Chen, Qingling Duan, Yeiqi Han
Abstract

As the fact that the emergence and development of crop nutrient deficiency has become more common nowadays, this research aims to find a method to segment and determine nutrient deficiency regions of crop images based on image processing technology. The experiment starts by obtaining 256 images of various crops such as oat, wheat, beet, maize, rye, potato, kidney been and sunflower with nutrient deficiency. Secondly all the experimental images are pre-processed by color transformation and enhancement to improve quality. Finally the nutrient deficiency diseased regions of crop images were segmented by fuzzy c-means clustering (FCM) algorithm based on fuzzy clustering algorithm. In the experimental course, color space of image was transformed from RGB to HSV and images were enhanced by use of median filter method, which not only remove the noise of the image, but also keep clear edge and efficiently highlight the disease regions. To test the accuracy of segmentation, other common algorithms such as threshold, edge detection and domain division were compared with FCM. Results showed that the FCM algorithm was the appropriate algorithm for segmentation of complexity and uncertainty images of crop disease. Applying fuzzy set theory in dividing the nutrient deficiency regions is the new point of the research, and this research has great practical significance in variable rate fertilization based on image processing technology.

Volume: 18, Issue: 8

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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