While the digital revolution has made huge volumes of high dimensional multimedia data available, it has also challenged users to extract the information they seek from heretofore unthinkably huge datasets. Traditional hard-computing data mining techniques have concentrated on flat-file applications. Soft-computing toolssuch as fuzzy sets, artificial neural networks, genetic algorithms, and rough setshowever, offer the opportunity to apply a wide range of data types to a variety of vital functions by handling real life uncertainty with low cost solutions. Multimedia compression techniques are useful for efficient management of very large databases. Data Mining: Concepts and Algorithms from Multimedia to Bioinformatics provides an accessible introduction to fundamental and advanced data mining technologies with an emphasis on handling multimedia data along with soft-computing. This readable survey describes data mining strategies for a slew of data types, including numeric and alpha-numeric formats, text, images, video, graphics, and the mixed representations therein. Along with traditional concepts and functions of data mining like classification, clustering, and rule miningthe authors highlight topical issues in Multimedia Applications and Bioinformatics, devoting a complete chapter to each subject. Principal topics discussed throughout the text include: Principles and classical algorithms on string matching and their role in data (mainly text) mining The role of soft-computing and its principles in data mining Data compression principles for both lossless and lossy techniques, including their scope in data mining Access of data using matching pursuits both in raw and compressed data domains Application in mining biological database