ABSTRACT
This chapter provides some background information about general data mining techniques so that the reader can have an understanding of the field. It presents the Markov model, support vector machines (SVM), artificial neural networks (ANN), association rule mining (ARM), the problem of multiclassification, as well as image classification, which are the aspects of image mining. SVM are learning systems that use a hypothesis space of linear functions in a high-dimensional feature space, trained with a learning algorithm from optimization theory. ANNs, like people, learn by example. The learning process in the human brain involves adjustments to the synaptic connections between neurons. An ANN simulates the biological nervous system in the human brain. ARM finds the relationships among itemsets based on their co-occurrence in the transactions. In many applications, there is one main problem in using ARM that is, using global minimum support. ARM techniques are used in general to make associations.
