A New Method for Initialising the K-Means Clustering Algorithm

2009 ◽  
Author(s):  
Xiaoping Qin ◽  
Shijue Zheng
Keyword(s):  
Clustering Algorithm ◽  
New Method

scholarly journals Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5697
Author(s):  
Chang Sun ◽  
Shihong Yue ◽  
Qi Li ◽  
Huaxiang Wang
Keyword(s):  
Clustering Algorithm ◽  
Two Phase Flow ◽  
Low Cost ◽  
Fast Response ◽  
New Method ◽  
Phase Flow ◽  
Reference Distribution ◽  
Two Phase ◽  
Long Time ◽  
Solid Liquid

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

scholarly journals Clustering Mixed Data by Fast Search and Find of Density Peaks

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shihua Liu ◽  
Bingzhong Zhou ◽  
Decai Huang ◽  
Liangzhong Shen
Keyword(s):  
Clustering Algorithm ◽  
New Method ◽  
Mixed Data ◽  
Fast Search ◽  
Density Peaks ◽  
Categorical Attributes

Aiming at the mixed data composed of numerical and categorical attributes, a new unified dissimilarity metric is proposed, and based on that a new clustering algorithm is also proposed. The experiment result shows that this new method of clustering mixed data by fast search and find of density peaks is feasible and effective on the UCI datasets.

Automatic Optimization Algorithm of Clusters Number Based on Maximum Distance

2014 ◽  
Vol 951 ◽  
pp. 231-234
Author(s):  
Hong Bo Zhou ◽  
Jun Tao Gao
Keyword(s):  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
New Method ◽  
Maximum Distance ◽  
Validity Index ◽  
Clustering Validity Index ◽  
Automatic Optimization ◽  
Clustering Validity

K-means clustering algorithm clusters datasets according to the certain clustering number k．However k cannot be confirmed beforehand.A new clustering validity index was designed from the standpoint of sample geometry.Based on the index a new method for determining the optimal clustering number in K-means clustering algorithm was proposed．

scholarly journals A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

2021 ◽  
Vol 13 (15) ◽  
pp. 2894
Author(s):  
Xiang Wu ◽  
Fengyan Wang ◽  
Mingchang Wang ◽  
Xuqing Zhang ◽  
Qing Wang ◽  
...  
Keyword(s):  
Rock Mass ◽  
Clustering Algorithm ◽  
Point Clouds ◽  
New Method ◽  
Rock Masses ◽  
Average Deviation ◽  
Density Peaks ◽  
3D Point Clouds ◽  
Discontinuity Sets ◽  
Dip Angle

Light detection and ranging (LiDAR) can quickly and accurately obtain 3D point clouds on the surface of rock masses, and on the basis of this, discontinuity information can be extracted automatically. This paper proposes a new method to automatically extract discontinuity information from 3D point clouds on the surface of rock masses. This method first applies the improved K-means algorithm based on the clustering algorithm by fast search and find of density peaks (DPCA) and the silhouette coefficient in the cluster validity index to identify the discontinuity sets of rock masses, and then uses the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to segment the discontinuity sets and to extract each discontinuity from a discontinuity set. Finally, the random sampling consistency (RANSAC) method is used to fit the discontinuities and to calculate their parameters. The 3D point clouds of the typical rock slope in the Rockbench repository is used to extract the discontinuity orientations using the new method, and these are compared with the results obtained from the classical approach and the previous automatic methods. The results show that, compared to the results obtained by Riquelme et al. in 2014, the average deviation of the dip direction and dip angle is reduced by 26% and 8%, respectively; compared to the results obtained by Chen et al. in 2016, the average deviation of the dip direction and dip angle is reduced by 39% and 40%, respectively. The method is also applied to an artificial quarry slope, and the average deviation of the dip direction and dip angle is 5.3° and 4.8°, respectively, as compared to the manual method. Furthermore, the related parameters are analyzed. The study shows that the new method is reliable, has a higher precision when identifying rock mass discontinuities, and can be applied to practical engineering.

scholarly journals A New Method to Enhance the Clustering Algorithm

2013 ◽  
pp. 120-122
Author(s):  
Abolfazl Zanghaei ◽  
Mohammadjavad Abolhassani ◽  
Alireza Ahmadian ◽  
Mohammad Reza Ay ◽  
Houshang Saberi
Keyword(s):  
Clustering Algorithm ◽  
New Method

scholarly journals A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm

2016 ◽  
pp. btw793 ◽  
Author(s):  
Joël Lafond-Lapalme ◽  
Marc-Olivier Duceppe ◽  
Shengrui Wang ◽  
Peter Moffett ◽  
Benjamin Mimee
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
De Novo ◽  
New Method ◽  
Hierarchical Clustering Algorithm

AN UNSUPERVISED KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM WITH KERNEL NORMALISATION

2004 ◽  
Vol 04 (04) ◽  
pp. 355-373 ◽  
Author(s):  
SHANG-MING ZHOU ◽  
JOHN Q. GAN
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
New Method ◽  
Fcm Algorithm ◽  
Input Space ◽  
Fuzzy C Means ◽  
Benchmark Datasets ◽  
Fuzzy C Means Clustering ◽  
Mercer Kernel

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.

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