scholarly journals AUTOMATED LONG-TERM POLYSOMNOGRAPHY ANALYSIS WITH WAVELET PROCESSING AND ADAPTIVE FUZZY CLUSTERING

2006 ◽  
Vol 18 (03) ◽  
pp. 119-123
Author(s):  
CHIH-FENG CHAO ◽  
JOE-AIR JIANG ◽  
MING-JANG CHIU ◽  
REN-GUEY LEE
Keyword(s):  
Fuzzy Clustering ◽  
Adaptive Method ◽  
Optimal Number ◽  
The Novel ◽  
Individual Subject ◽  
Adaptive Fuzzy ◽  
Novel Method ◽  
Effective Visualization ◽  
Optimal Number Of Clusters

To assist in the inspection of sleep-related diagnosis and research, an adaptive method for processing long-term polysomnography (PSG) is proposed in this paper. The extracted features of segmented PSG based on wavelet analysis can be used for clustering the segments with similar pattern into a group. The adaptive fuzzy clustering was used to estimate the clusters within the PSG recordings, the optimal number of clusters and the optimal features of an individual subject. The novel method with the adaptive-to-subject concept exhibits four advantages in comparison with other approaches: (1) Full automated, (2) adaptive to the diversity of physiological signals among subjects, (3) less sensitive to noise and artifacts, (4) effective visualization of analysis results for clinicians. The simulation results show the superiority of the proposed method in long-term PSG analysis.

scholarly journals Victims of War: Dehydroepiandrosterone Concentrations in Hair and Their Associations with Trauma Sequelae in Palestinian Adolescents Living in the West Bank

2019 ◽  
Vol 9 (2) ◽  
pp. 20 ◽  
Author(s):  
Lena Schindler ◽  
Mohammed Shaheen ◽  
Rotem Saar-Ashkenazy ◽  
Kifah Bani Odeh ◽  
Sophia-Helen Sass ◽  
...  
Keyword(s):  
Traumatic Stress ◽  
West Bank ◽  
The Novel ◽  
The West ◽  
Hair Samples ◽  
Novel Method ◽  
Reported Data ◽  
And Coping ◽  
Trauma Sequelae

Due to its anti-glucocorticoid properties, the steroid hormone dehydroepiandrosterone (DHEA) might play a role for coping with traumatic stress and posttraumatic stress disorder (PTSD). The majority of studies report elevated DHEA secretion and decreased cortisol/DHEA ratio associated with traumatic stress, however, contrasting results have also been published. One reason for this heterogeneity might be that in past studies, DHEA has been measured in plasma or saliva samples reflecting acute hormone levels. In comparison, the current study assessed the hair levels of DHEA and cortisol as long-term markers along with self-reported data on psychopathology and coping in 92 female adolescents aged 11–16 from the West Bank affected by the Israeli–Palestinian conflict. Results showed that trauma-exposed individuals had significantly higher DHEA levels (p = 0.013) and lower cortisol/DHEA ratios (p = 0.036) than participants from the non-trauma group. Furthermore, DHEA and cortisol/DHEA ratio emerged as associated with trauma load and timing, but not with coping. By applying the novel method of DHEA analysis from hair samples, this study adds to the growing literature on the interplay of DHEA, cortisol, traumatic stress and coping, and provides valuable starting points for further research.

scholarly journals A Validity Index for Fuzzy Clustering Based on Bipartite Modularity

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yongli Liu ◽  
Xiaoyang Zhang ◽  
Jingli Chen ◽  
Hao Chao
Keyword(s):  
Fuzzy Clustering ◽  
Optimal Number ◽  
Experimental Results ◽  
Validity Index ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Noise Data ◽  
Clustering Validity ◽  
Optimal Number Of Clusters

Because traditional fuzzy clustering validity indices need to specify the number of clusters and are sensitive to noise data, we propose a validity index for fuzzy clustering, named CSBM (compactness separateness bipartite modularity), based on bipartite modularity. CSBM enhances the robustness by combining intraclass compactness and interclass separateness and can automatically determine the optimal number of clusters. In order to estimate the performance of CSBM, we carried out experiments on six real datasets and compared CSBM with other six prominent indices. Experimental results show that the CSBM index performs the best in terms of robustness while accurately detecting the number of clusters.

scholarly journals A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

2017 ◽  
Vol 65 (4) ◽  
pp. 359-365 ◽  
Author(s):  
Javier Senent-Aparicio ◽  
Jesús Soto ◽  
Julio Pérez-Sánchez ◽  
Jorge Garrido
Keyword(s):  
Frequency Analysis ◽  
Fuzzy Clustering ◽  
Optimal Number ◽  
Regional Frequency Analysis ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Homogeneous Regions ◽  
Optimal Number Of Clusters

AbstractOne of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.

scholarly journals A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Min Ren ◽  
Peiyu Liu ◽  
Zhihao Wang ◽  
Jing Yi
Keyword(s):  
Adaptive Method ◽  
Convergence Result ◽  
Decision Function ◽  
Optimal Number ◽  
Validity Index ◽  
Number Of Clusters ◽  
Clustering Validity Index ◽  
Clustering Validity ◽  
Optimal Number Of Clusters ◽  
Self Adaptive

For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

scholarly journals Segmentation of Life Insurance Customers Based on their Profile Using Fuzzy Clustering

2015 ◽  
Vol 61 ◽  
pp. 17-24
Author(s):  
Gholamreza Jandaghi ◽  
Zahra Moradpour
Keyword(s):  
Fuzzy Clustering ◽  
Life Insurance ◽  
Optimal Number ◽  
Business Strategies ◽  
Insurance Company ◽  
Business World ◽  
Mining Projects ◽  
Traditional Approaches ◽  
Mass Marketing ◽  
Optimal Number Of Clusters

In the current competitive environment, companies will be able to adjust business strategies, they use market segmentation based on practical ways rather than using traditional approaches or incomplete and impractical mass marketing. In recent years, mining has gained attention and popularity in the business world. The goal of data mining projects is to convert the raw data into useful information. Clustering can also be used to explore differences in attitudes and intentions of the clients. In this study, we used fuzzy clustering on 1071 life insurance customers during March to October 2014. . Results show that the optimal number of clusters was 2 which were named as "investment" and "life safety". Some suggestions are presented to improve the performance of the insurance company.

GIVING FUZZINESS TO SPATIAL CLUSTERS: A NEW INDEX FOR CHOOSING THE OPTIMAL NUMBER OF CLUSTERS

2013 ◽  
Vol 22 (03) ◽  
pp. 1350009 ◽  
Author(s):  
GEORGE GREKOUSIS
Keyword(s):  
Fuzzy Clustering ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Fuzzy Cluster ◽  
Cluster Validation ◽  
Number Of Clusters ◽  
A Value ◽  
Membership Value ◽  
Optimal Number Of Clusters

Choosing the optimal number of clusters is a key issue in cluster analysis. Especially when dealing with more spatial clustering, things tend to be more complicated. Cluster validation helps to determine the appropriate number of clusters present in a dataset. Furthermore, cluster validation evaluates and assesses the results of clustering algorithms. There are numerous methods and techniques for choosing the optimal number of clusters via crisp and fuzzy clustering. In this paper, we introduce a new index for fuzzy clustering to determine the optimal number of clusters. This index is not another metric for calculating compactness or separation among partitions. Instead, the index uses several existing indices to give a degree, or fuzziness, to the optimal number of clusters. In this way, not only do the objects in a fuzzy cluster get a membership value, but the number of clusters to be partitioned is given a value as well. The new index is used in the fuzzy c-means algorithm for the geodemographic segmentation of 285 postal codes.

scholarly journals Novel method for determining <sup>234</sup>U–<sup>238</sup>U ages of Devils Hole 2 cave calcite (Nevada)

Geochronology ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 49-58
Author(s):  
Xianglei Li ◽  
Kathleen A. Wendt ◽  
Yuri Dublyansky ◽  
Gina E. Moseley ◽  
Christoph Spötl ◽  
...  
Keyword(s):  
The Novel ◽  
Time Intervals ◽  
Δ13c Values ◽  
Devils Hole ◽  
Calcite Deposition ◽  
Dating Method ◽  
Novel Method ◽  
Multi Linear Regression ◽  
Δ18o And Δ13c

Abstract. Uranium–uranium (234U–238U) disequilibrium dating can determine the age of secondary carbonates over greater time intervals than the well-established 230Th–234U dating method. Yet it is rarely applied due to unknowns in the initial δ234U (δ234Ui) value, which result in significant age uncertainties. In order to understand the δ234Ui in Devils Hole 2 cave, Nevada, we have determined 110 δ234Ui values from phreatic calcite using 230Th–234U disequilibrium dating. The sampled calcite was deposited in Devils Hole 2 between 4 and 590 ka, providing a long-term look at δ234Ui variability over time. We then performed multi-linear regression among the δ234Ui values and correlative δ18O and δ13C values. The regression can be used to estimate the δ234Ui value of Devils Hole calcite based upon its measured δ18O and δ13C values. Using this approach and the measured present-day δ234U values of Devils Hole 2 calcite, we calculated 110 independent 234U–238U ages. In addition, we used newly measured δ18O, δ13C, and present-day δ234U values to calculate 10 234U–238U ages that range between 676 and 731 ka, thus allowing us to extend the Devils Hole chronology beyond the 230Th–234U-dated chronology while maintaining an age precision of ∼ 2 %. Our results indicate that calcite deposition at Devils Hole 2 cave began no later than 736 ± 11 kyr ago. The novel method presented here may be applied to future speleothem studies in similar hydrogeological settings, given appropriate calibration studies.

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