A Comparative Study of Data Mining Algorithms for Image Classification

• Published in: International Journal of Education and Management Engineering Vol. 5; no. 2; pp. 1 - 9
• Main Authors: Thamilselvana, P, Sathiaseelan, J. G. R.
• Format: Journal Article
• Language: English
• Published: Hong Kong Modern Education and Computer Science Press 08.06.2015
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Classification; Cognitive Development; College Science; Comparative Analysis; Comparative Education; Comparative studies; Computer Science; Data mining; Database Management Systems; Educational Technology; Feature extraction; Genetic algorithms; Image classification; Image segmentation; Inspection; Kappa coefficient; Medical imaging; Navigation; Neural networks; Object recognition; Pattern Recognition; Regression (Statistics); Remote sensing; Supervision
• ISSN: 2305-3623; 2305-8463; 2305-8463
• DOI: 10.5815/ijeme.2015.02.01

Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It's also includes image preprocessing, feature extraction, object detection, object classification, object segmentation, object classification and many more techniques. Image classification to produce the accurate prediction results in their target class for each case in the data. It is a very predominant and challenging task in various application domains, including video surveillance, biometry, biomedical imaging, industrial visual inspection, vehicle navigation, remote sensing and robot navigation. The aim of this study compares the some predominant data mining algorithms in image classification. For this review SVM, AdaBoost, CART, KNN, Artificial Neural Network, K-Means, Chaos Genetic Algorithm, EM Algorithm, C4.5 algorithms are taken.