A Hybrid Incremental Clustering Method-Combining Support Vector Machine and Enhanced Clustering by Committee Clustering Algorithm

2007 ◽  
pp. 465-472
Author(s):  
Deng-Yiv Chiu ◽  
Kong-Ling Hsieh
Keyword(s):  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Support Vector ◽  
Incremental Clustering ◽  
Clustering Method

Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

2020 ◽  
Vol 10 (2) ◽  
pp. 21-39
Author(s):  
Archana Yashodip Chaudhari ◽  
Preeti Mulay
Keyword(s):  
Smart Grids ◽  
Clustering Algorithm ◽  
Incremental Clustering ◽  
Clustering Method ◽  
Data Types ◽  
Dynamic Data ◽  
Real Dataset ◽  
Processing Power ◽  
Microsoft Azure ◽  
Socioeconomic Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

scholarly journals Support Vector Machine based on clustering algorithm for interruptible load forecasting

2019 ◽  
Vol 533 ◽  
pp. 012018 ◽  
Author(s):  
Xiang Yu ◽  
Guangfeng Bu ◽  
Bingyue Peng ◽  
Chen Zhang ◽  
Xiaolan Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Interruptible Load

scholarly journals Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

2020 ◽  
Vol 17 (15) ◽  
pp. 5497
Author(s):  
Khaled Assi ◽  
Syed Masiur Rahman ◽  
Umer Mansoor ◽  
Nedal Ratrout
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Injury Severity ◽  
Support Vector ◽  
Severity Level ◽  
Feed Forward ◽  
Fuzzy C Means ◽  
Severity Prediction ◽  
Crash Injury Severity

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models.

scholarly journals Fungal Disease in Cotton Leaf Detection and Classification using Neural Networks and Support Vector Machine

2019 ◽  
Vol 8 (10) ◽  
pp. 3664-3667 ◽  
Keyword(s):  
Support Vector Machine ◽  
Production Rate ◽  
Clustering Algorithm ◽  
Economic Status ◽  
Fungal Disease ◽  
Support Vector ◽  
Svm Classifier ◽  
Disease Detection ◽  
Cotton Leaf ◽  
Average Accuracy

Agriculture productivity is the main factor for improving economic status of India. Reduction in production rate is mainly due to various diseases in plants. Identification of plant disease in early stage is the main challenge for improving the production rate as well as economic status. This paper presents automatic disease detection in cotton crop for three types of diseases Alternaria Leaf Spot Fungal Disease (ALSFD), Grey Mildew Cotton Disease (GMCD), and Rust Foliar Fungal Disease (RFFD). The K-means clustering algorithm is used for disease segmentation for cotton leaf. The diseased cluster is segmented into three clusters. From cluster 2 the features Mean , Contrast, Energy, Correlation, Standard Deviation, Variance , Entropy, and Kurtosis are extracted. The extracted features for 30 samples are given to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers for disease classification. The performance of these classifiers are compared. The ALSF disease is classified 77.4% for ANN and 84.3% for SVM, GMC disease is 87.8% for ANN and 98.7% in SVM, RFF disease is 90.1%for ANN and 93.2% for SVM. The overall average accuracy of ANN classifier is 85.1% for three diseases and overall average accuracy for SVM is 92.06% for three diseases. It is clearly observed from the analysis SVM classifier gives accurate disease detection compared to ANN.

scholarly journals Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaochen Zhang ◽  
Dongxiang Jiang ◽  
Te Han ◽  
Nanfei Wang ◽  
Wenguang Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Imbalanced Data ◽  
Principal Component ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Diagnosis Model ◽  
Sample Set

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

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