scholarly journals Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture

2019 ◽  
Vol 9 (2S) ◽  
pp. 9-13
Keyword(s):  
Cluster Analysis ◽  
Precision Agriculture ◽  
Crop Production ◽  
Clustering Algorithm ◽  
Research Work ◽  
K Value ◽  
Clustering Technique ◽  
Production Analysis ◽  
Initial Cluster ◽  
The Stability

The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

Clustering Algorithm Based on Improved Particle Swarm Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 689-692 ◽  
Author(s):  
Jin Hui Yang ◽  
Xi Cao
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Initial Cluster ◽  
Cluster Analysis Method ◽  
Swarm Algorithm

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

scholarly journals Research on Density-Based K-means Clustering Algorithm

2021 ◽  
Vol 2137 (1) ◽  
pp. 012071
Author(s):  
Shuxin Liu ◽  
Xiangdong Liu
Keyword(s):  
Clustering Algorithm ◽  
Cluster Center ◽  
Basic Principles ◽  
K Value ◽  
Advantages And Disadvantages ◽  
Dbscan Algorithm ◽  
Initial Cluster ◽  
Development Direction ◽  
Density Clustering ◽  
Improved Algorithm

Abstract Cluster analysis is an unsupervised learning process, and its most classic algorithm K-means has the advantages of simple principle and easy implementation. In view of the K-means algorithm’s shortcoming, where is arbitrary processing of clusters k value, initial cluster center and outlier points. This paper discusses the improvement of traditional K-means algorithm and puts forward an improved algorithm with density clustering algorithm. First, it describes the basic principles and process of the K-means algorithm and the DBSCAN algorithm. Then summarizes improvement methods with the three aspects and their advantages and disadvantages, at the same time proposes a new density-based K-means improved algorithm. Finally, it prospects the development direction and trend of the density-based K-means clustering algorithm.

Quantum Computing Based Technique for Cancer Disease Detection System

2016 ◽  
pp. 609-629
Author(s):  
Setu Kumar Chaturvedi ◽  
Milan Jain
Keyword(s):  
Quantum Computing ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Detection System ◽  
Research Work ◽  
Older Individuals ◽  
Cancer Disease ◽  
Clustering Technique ◽  
Shor's Algorithm ◽  
Aging Populations

Barring any cancer prevention breakthroughs, the expansion of the aged population will likely increase number of older individuals diagnosed for cancer in the coming decades. Dimensions of the cancer burden and its devastating manner of challenge ahead are inferred in the context of with aging populations to underscore the possible increase that demographic factors may have on the magnitude of the cancer problem for older persons in the future years. Presently the detection procedure is very time consuming and not accurate, in this respect there is a need of more accurate, fast and efficient method through computing technologies. The present research work incorporates quantum computing with clustering algorithm i.e. Shor's algorithm of quantum computing with hierarchical clustering technique. Here adaptation of Shor's algorithm helps to increase accuracy, and hierarchical clustering technique helps to detect the stages of cancer.

A Clustering Algorithm Based on Improved Particle Swarm Optimization

2014 ◽  
Vol 635-637 ◽  
pp. 1467-1470
Author(s):  
Xun Wang
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Initial Value ◽  
Swarm Optimization ◽  
Initial Cluster ◽  
Cluster Analysis Method

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

Quantum Computing Based Technique for Cancer Disease Detection System

2016 ◽  
pp. 396-416
Author(s):  
Setu Kumar Chaturvedi ◽  
Milan Jain
Keyword(s):  
Quantum Computing ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Detection System ◽  
Research Work ◽  
Older Individuals ◽  
Cancer Disease ◽  
Clustering Technique ◽  
Shor's Algorithm ◽  
Aging Populations

Barring any cancer prevention breakthroughs, the expansion of the aged population will likely increase number of older individuals diagnosed for cancer in the coming decades. Dimensions of the cancer burden and its devastating manner of challenge ahead are inferred in the context of with aging populations to underscore the possible increase that demographic factors may have on the magnitude of the cancer problem for older persons in the future years. Presently the detection procedure is very time consuming and not accurate, in this respect there is a need of more accurate, fast and efficient method through computing technologies. The present research work incorporates quantum computing with clustering algorithm i.e. Shor's algorithm of quantum computing with hierarchical clustering technique. Here adaptation of Shor's algorithm helps to increase accuracy, and hierarchical clustering technique helps to detect the stages of cancer.

Clustering Algorithm Based on Improved Particle Swarm Optimization

2013 ◽  
Vol 765-767 ◽  
pp. 486-488
Author(s):  
Hong Chun Wang ◽  
Feng Wen Wen ◽  
Feng Song
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Clustering Algorithm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Initial Cluster ◽  
Area Of Influence ◽  
Strong Capacity

K-means algorithm has therefore become one of the methods widely used in cluster analysis. But the classification results of K-means algorithm depend on the initial cluster centers choice. We present a new neighborhood for PSO methods called the area of influence (AOI) and consider the combination of K-means has strong capacity of local searching and PSO has power global search ability. The improved PSO, i.e., improves the K-means local searching capacity, accelerates the convergence rate, and prevents the premature convergence effectively.

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Employment Pattern and Extent of Under-utilisation of Manpower in Rural Baran; Areas of Pakistan

1993 ◽  
Vol 32 (4II) ◽  
pp. 1225-1233
Author(s):  
Sabur Ghayur
Keyword(s):  
Crop Production ◽  
Labour Force ◽  
Research Work ◽  
Labour Force Participation ◽  
Hydroelectric Power ◽  
Economic Activities ◽  
North West ◽  
North West Frontier Province ◽  
Physical Infrastructure ◽  
Employment Pattern

The barani (rain-fed) region accounts for about a fifth of the cultivated area in Pakistan. It has the potential to significantly increase crop production levels. Similarly, considerable scope exists in this area for the development of forests, fruit and vegetable gardening, pasture and stock rearing. Most of the natural resources are also found in this tract. Its hilly areas possess a vast potential for tourism. Besides, significant opportunities exist for irrigation and hydroelectric power generation. An optimum utilisation of all this potential, obviously, is employmentgenerating and income-augmenting. Despite all such realisations this region as a whole, unfortunately, is identified as the least attended to area in terms of provision of socio-physical infrastructure, other development programmes and, even, research work. This led to a deterioration of the employment situation in the barani region as a whole. A poor information base and analysis thereof on employment and manpower related variables is also the consequence of such a treatment to this area. I This paper, using the data of a field survey, tries to fill, though partly, the vacuum on employment and related variables in the rural barani region. An attempt is made here to record and analyse the labour force participation rates, employment pattern (main economic activities) and unemployment/underemployment levels prevailing in the rural baran; areas of the provinces of the Punjab and North-West Frontier Province (NWFP).

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