An Improved K-means Clustering Algorithm Based on the Voronoi Diagram Method

Author(s):  
Jiuyuan Huo ◽  
Honglei Zhang
2017 ◽  
Vol 32 (6) ◽  
pp. 4644-4655 ◽  
Author(s):  
Weihong Huang ◽  
Kai Sun ◽  
Junjian Qi ◽  
Jiaxin Ning

1998 ◽  
Author(s):  
Q. M. Jonathan Wu ◽  
Xiaotian Shi ◽  
Ali Jerbi ◽  
Chander P. Grover

Author(s):  
Gokhan Bayar

Purpose The purpose of this paper is to develop a methodology for detecting tree trunks for autonomous agricultural applications performed using mobile robots. Design/methodology/approach The system is constructed by following the principles of Voronoi diagram method which is one of the machine learning algorithms used by the robotics, mechatronics and automation researchers. Findings To analyze the accuracy and performance and to make verification and evaluation, both simulation and experimental studies are conducted. The results indicate that the tree trunk detection system developed using Voronoi diagram method can be able to detect tree trunks with high precision. Originality/value A novel solution technique to detect tree trunks is proposed. The adaptation of Voronoi diagram method in an agricultural (orchard) task is presented.


2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.


Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%


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