ABSTRACT
Multimedia data mining is an interdisciplinary research field in which generic data mining theory and techniques are applied to the multimedia data to facilitate multimedia-specific knowledge discovery tasks. In this chapter, commonly used and recently developed generic statistical learning theory, concepts, and techniques in recent multimedia data mining literature are introduced and their pros and cons are discussed. The principles and uniqueness of the applications of these statistical data learning and mining techniques to the multimedia domain are also provided in this chapter. Data mining is defined as discovering hidden information in a data set.
Like data mining in general, multimedia data mining involves many different algorithms to accomplish different tasks. All of these algorithms attempt to fit a model to the data. The algorithms examine the data and determine a model that is closest to the characteristics of the data being examined. Typical data mining algorithms can be characterized as consisting of three components:
