scholarly journals A Novel Algorithm for Initial Cluster Center Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 74683-74693 ◽  
Author(s):  
Yating Li ◽  
Jianghui Cai ◽  
Haifeng Yang ◽  
Jifu Zhang ◽  
Xujun Zhao
Keyword(s):  
Cluster Center ◽  
Initial Cluster ◽  
Novel Algorithm

scholarly journals Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ziqi Jia ◽  
Ling Song
Keyword(s):  
Categorical Data ◽  
Clustering Algorithm ◽  
Numerical Data ◽  
Experimental Results ◽  
Cluster Center ◽  
Real Dataset ◽  
Dissimilarity Coefficient ◽  
Initial Cluster ◽  
Data Objects ◽  
Selection Of

The k-prototypes algorithm is a hybrid clustering algorithm that can process Categorical Data and Numerical Data. In this study, the method of initial Cluster Center selection was improved and a new Hybrid Dissimilarity Coefficient was proposed. Based on the proposed Hybrid Dissimilarity Coefficient, a weighted k-prototype clustering algorithm based on the hybrid dissimilarity coefficient was proposed (WKPCA). The proposed WKPCA algorithm not only improves the selection of initial Cluster Centers, but also puts a new method to calculate the dissimilarity between data objects and Cluster Centers. The real dataset of UCI was used to test the WKPCA algorithm. Experimental results show that WKPCA algorithm is more efficient and robust than other k-prototypes algorithms.

scholarly journals Analisis Clustering Pengelompokan Penjualan Paket Data Menggunakan Metode K-Means

2021 ◽  
Vol 13 (1) ◽  
pp. 33-38
Author(s):  
Dimas Galang Ramadhan ◽  
Indri Prihatini ◽  
Febri Liantoni
Keyword(s):  
Cluster Analysis ◽  
Cluster Center ◽  
Active Period ◽  
Analysis Algorithm ◽  
Initial Cluster

At present with the COVID-19 pandemic situation that requires all activities based in the network, starting from work, college, school, everything is based on the network. Certain provider users will experience excessive data plan usage. This also has an effect on a counter that sells data packages, which must provide several data package services in accordance with current conditions. This research was conducted to analyze the grouping of sales of data packages that are often purchased by customers in a counter by using the K-Means method. The K-Means method is used because the K-Means algorithm is not influenced by the order of the objects used, this is proven when the writer tries to determine the initial cluster center randomly from one of the objects in the first calculation. sales of data packages at a counter. Variables used include Price, Active period, and number of data packages. The K-Means Cluster Analysis algorithm is basically applied to the problem of understanding consumer needs, identifying the types of data package products that are often purchased. The K-Means algorithm can be used to describe the characteristics of each group by summarizing a large number of objects so that it is easier.

Clustering Based on NMTF Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 2365-2369
Author(s):  
Lei Huang ◽  
Chan Le Wu
Keyword(s):  
Parameter Estimation ◽  
Cluster Center ◽  
Data Partition ◽  
Clustering Methods ◽  
Parameter Estimation Problem ◽  
Data Set ◽  
Initial Cluster ◽  
Meaning Representation ◽  
Good Cluster ◽  
Representation Group

NMTF(Normalizing Mapping Training Framework) operates by mapping initial cluster centers and then iteratively training points to clusters base on the proximate cluster center and updating cluster centers. we regard finding good cluster centers as a normalizing parameter estimation problem then constructing the parameters of other normalizing models yields a space of novel clustering methods. In this paper we propose the idea using abstract of a text to members of a data partition in place of estimation of cluster centers. The method can accurately reconstruct meaning representation group used to generate a given data set.

Multi-Channel Mapping Image Segmentation Method Based on LDA

2020 ◽  
pp. 2154012
Author(s):  
Qiu-Xia Hu ◽  
Jie Tian ◽  
Dong-Jian He
Keyword(s):  
Plant Disease ◽  
Experimental Result ◽  
Cluster Center ◽  
Segmentation Method ◽  
Linear Discriminant ◽  
Linear Discrimination ◽  
Initial Cluster ◽  
Segmentation Accuracy ◽  
Eigen Values ◽  
Better Than

In order to improve the segmentation accuracy of plant lesion images, multi-channels segmentation algorithm of plant disease image was proposed based on linear discriminant analysis (LDA) method’s mapping and K-means’ clustering. Firstly, six color channels from RGB model and HSV model were obtained, and six channels of all pixels were laid out to six columns. Then one of these channels was regarded as label and the others were regarded as sample features. These data were grouped for linear discrimination analysis, and the mapping values of the other five channels were applied to the eigen vector space according to the first three big eigen values. Secondly, the mapping value was used as the input data for K-means and the points with minimum and maximum pixel values were used as the initial cluster center, which overcame the randomness for selecting the initial cluster center in K-means. And the segmented pixels were changed into background and foreground, so that the proposed segmentation method became the clustering of two classes for background and foreground. Finally, the experimental result showed that the segmentation effect of the proposed LDA mapping-based method is better than those of K-means, ExR and CIVE methods.

A Center Initialization Method Based on Merger of Divisions of a Data Set along Each Dimension

2013 ◽  
Vol 333-335 ◽  
pp. 1269-1272
Author(s):  
Guang Hui Chen
Keyword(s):  
Real Data ◽  
Cluster Center ◽  
Data Sets ◽  
Data Set ◽  
Initial Cluster ◽  
Data Point

this paper proposes a hierarchical division method that divides a data set into two subsets along each dimension, and merges them into a division of the data set. Then the initial cluster centers are located in dense and separate subsets of the data set, and the means of data point in these subsets are selected as the initial cluster centers. Thus a new cluster center initialization method is developed. Experiments on real data sets show that the proposed cluster center initialization method is desirable.

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