Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria.
In this paper, computer-based methods for defining tumor region in the brain using MRI images is presented. Brain tumor detection is a most important area in medical image processing. Various methods are used to know whether a brain having tumor or not. Magnetic resonance (MR) imaging is currently an indispensable diagnostic imaging technique in the study of the human brain . It is a non-invasive technique that provides fairly good contrast resolution for different tissues and generates an extensive information pool about the condition of the brain. Classification of human brain in magnetic resonance (MR) images is possible via supervised techniques such as artificial neural networks and support vector machine (SVM) , and unsupervised classification techniques unsupervised such as self organization map (SOM) and fuzzy c-means combined with feature extraction techniques. Other supervised classification techniques, such as k-nearest neighbors (k-NN) also group pixels based on their similarities in each feature image can be used to classify the normal/pathological T2-wieghted MRI images.