tailieunhanh - Multimedia Data Mining 3

Chapter 4 (MDM) | Chapter 3 Statistical Mining Theory and Techniques Introduction Multimedia data mining is an interdisciplinary research field in which generic data mining theory and techniques are applied to the multimedia data to fa- cilitate multimedia-specific knowledge discovery tasks. In this chapter, com- monly used and recently developed generic statistical learning theory, con- cepts, and techniques in recent multimedia data mining literature are intro- duced 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: Model: The purpose of the algorithm is to fit a model to the data. • Preference: Some criteria must be used to select one model over another. • Search: All the algorithms require searching the data. • The model in data mining can be either predictive or descriptive in nature. A predictive model makes a prediction about values of data using known results found from different data sources. A descriptive model identifies patterns or relationships in data. Unlike the predictive model, a descriptive model serves as a way to explore the properties of the data examined, not to predict new properties. There are many different statistical methods used to accommodate different multimedia data mining tasks. These methods not only require specific types of data structures, but also imply certain types of algorithmic approaches. The statistical learning theory and techniques introduced .