Classification Performances Of Data Mining Clustering Algorithms For Remotely Sensed Multispectral Image Data








Abstract

This study compares classification algorithm performances of data mining clustering algorithms for remotely sensed multispectral image data using WEKA data mining software. Clustering algorithm selection is very important for data mining classification method based clustering. The class attribute for remotely sensed multispectral image data is obtained from six different clustering algorithms for classification. Classification algorithm performances computed depending on the data labeling of six different clustering algorithms in terms of correctly classified instances and kappa statistics for seven different classification algorithms.A strategy is developed for selecting the best unsupervised clustering algorithm, among different clustering algorithms, giving the highest supervised classification accuracy in terms of correctly classified instances and kappa statistics for semi-supervised classification of remotely-sensed multispectral image data. The performances of seven semi-supervised classification methods assessed depending on six different unsupervised clustering algorithms for supervised classification of remotely sensed multispectral image data. This study determines data free clustering algorithms for classification.


Modules


Algorithms


Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL