Application of Improved K-Means Algorithm Density in the Grades of Cultivated Land Fertility Evaluation

2014 ◽  
Vol 644-650 ◽  
pp. 2047-2050
Author(s):  
Li Ying Cao ◽  
He Long Yu ◽  
Gui Fen Chen ◽  
Ting Ting Yang
Keyword(s):  
Soil Fertility ◽  
Precision Agriculture ◽  
Cluster Center ◽  
Selection Algorithm ◽  
Density Region ◽  
Data Set ◽  
Agriculture Soil ◽  
Initial Cluster ◽  
High Density Region ◽  
Clustering Center

precision agriculture, soil fertility evaluation is the foundation of variable rate fertilization, the initial clustering centers of K means algorithm soil fertility levels in the traditional evaluation methods generated randomly from the data set, the clustering result is not stable. This paper proposes an improved K-means algorithm density algorithm to optimize the initial clustering center selection algorithm based on K, the most far away to each other in high density region point as the initial cluster center. Experiments show that, the improved K-means algorithm can eliminate the dependence on the initial cluster center; the clustering result has been greatly improved.

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.

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.

Fuzzy C-Means Clustering Algorithm Based on Coefficient of Variation

2014 ◽  
Vol 998-999 ◽  
pp. 873-877
Author(s):  
Zhen Bo Wang ◽  
Bao Zhi Qiu
Keyword(s):  
Coefficient Of Variation ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Data ◽  
Cluster Center ◽  
Data Set ◽  
Fuzzy C Means ◽  
Initial Cluster ◽  
Fuzzy C Means Clustering ◽  
The Impact

To reduce the impact of irrelevant attributes on clustering results, and improve the importance of relevant attributes to clustering, this paper proposes fuzzy C-means clustering algorithm based on coefficient of variation (CV-FCM). In the algorithm, coefficient of variation is used to weigh attributes so as to assign different weights to each attribute in the data set, and the magnitude of weight is used to express the importance of different attributes to clusters. In addition, for the characteristic of fuzzy C-means clustering algorithm that it is susceptible to initial cluster center value, the method for the selection of initial cluster center based on maximum distance is introduced on the basis of weighted coefficient of variation. The result of the experiment based on real data sets shows that this algorithm can select cluster center effectively, with the clustering result superior to general fuzzy C-means clustering algorithms.

scholarly journals Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6889
Author(s):  
Yuxin Huang ◽  
Jingdao Fan ◽  
Zhenguo Yan ◽  
Shugang Li ◽  
Yanping Wang
Keyword(s):  
Association Rules ◽  
Early Warning ◽  
Association Rule ◽  
Cluster Center ◽  
Apriori Algorithm ◽  
Data Set ◽  
Early Warning Model ◽  
Initial Cluster ◽  
Different Dimensions ◽  
Warning Model

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.

Incremental kernel fuzzy c-means with optimizing cluster center initialization and delivery

Kybernetes ◽  
2016 ◽  
Vol 45 (8) ◽  
pp. 1273-1291 ◽  
Author(s):  
Runhai Jiao ◽  
Shaolong Liu ◽  
Wu Wen ◽  
Biying Lin
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Accurate Information ◽  
Incremental Clustering ◽  
Data Set ◽  
Content Type ◽  
Fuzzy C Means ◽  
Initial Cluster

Purpose The large volume of big data makes it impractical for traditional clustering algorithms which are usually designed for entire data set. The purpose of this paper is to focus on incremental clustering which divides data into series of data chunks and only a small amount of data need to be clustered at each time. Few researches on incremental clustering algorithm address the problem of optimizing cluster center initialization for each data chunk and selecting multiple passing points for each cluster. Design/methodology/approach Through optimizing initial cluster centers, quality of clustering results is improved for each data chunk and then quality of final clustering results is enhanced. Moreover, through selecting multiple passing points, more accurate information is passed down to improve the final clustering results. The method has been proposed to solve those two problems and is applied in the proposed algorithm based on streaming kernel fuzzy c-means (stKFCM) algorithm. Findings Experimental results show that the proposed algorithm demonstrates more accuracy and better performance than streaming kernel stKFCM algorithm. Originality/value This paper addresses the problem of improving the performance of increment clustering through optimizing cluster center initialization and selecting multiple passing points. The paper analyzed the performance of the proposed scheme and proved its effectiveness.

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Performance Evaluation of Artificial Intelligence on Soil Property Detection

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Smriti Singhatiya ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Artificial Intelligence ◽  
Precision Agriculture ◽  
Crop Production ◽  
Nutrient Status ◽  
Soil Testing ◽  
Support Vector ◽  
Soil Test ◽  
New Era ◽  
Agriculture Soil ◽  
Historical Role

Now-a-days there is a need to study the nutrient status in lower horizons of the soil. Soil testing has played historical role in evaluating soil fertility maintenance and in sustainable agriculture. Soil testing shall also play its crucial role in precision agriculture. At present there is a need to develop basic inventory as per soil test basis and necessary information has to be built into the system for translating the results of soil test to achieve the crop production goal in new era. To achieve this goal artificial intelligence approach is used for predicting the soil properties.  In this paper for analysing these properties support vector regression (SVR), ensembled regression (ER) and neural network (NN) are used. The performance is evaluated with respect to MSE and RMSE and it is observed that ER outperforms better with respect to SVR and NN.

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.

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