Multimedia Data Mining Trends and Challenges

The development of information technology is particularly noticeable in the methods and techniques of data acquisition. Data can be stored in many forms of digital media, for example, still images taken by a digital camera, MP3 songs, or MPEG videos from desktops, cell phones, or video cameras. Data volumes are growing at different speeds with the fastest Internet and multimedia resource growth. In these fast growing volumes of digital data environments, restrictions are connected with a human’s low data complexity and dimensionality analysis. The article begins with a short introduction to data mining, considering different kinds of data, both structured as well as semistructured and unstructured. It emphasizes the special role of multimedia data mining. Then, it presents a short overview of data mining goals, methods, and techniques used in multimedia data mining. This section focuses on a brief discussion on supervised and unsupervised classification, uncovering interesting rules, decision trees, artificial neural networks, and rough-neural computing. The next section presents advantages offered by multimedia data mining and examples of practical and successful applications. It also contains a list of application domains. The following section describes multimedia data mining critical issues, summarizes main multimedia data mining advantages and disadvantages, and considers some predictive trends.