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The success of the digital revolution and the growth of the Internet have ensured that huge volumes of high-dimensional multimedia data are available all around us. The World Wide Web has played an important role in making the data, even from geographically distant locations, easily accessible to users all over the world. However, often most of this data are not of much interest to most of the users. The problem is to mine useful information or patterns from the huge datasets. Data mining refers to this process of extracting knowledge that is of interest to the user. Data mining is an evolving and growing area of research and development, both in academia as well as in industry. It involves interdisciplinary research and development encompassing diverse domains. In this age of multimedia data exploration, data mining should no longer be restricted to the mining of knowledge from large volumes of high-dimensional datasets in traditional databases only. Researchers need to pay attention to the mining of different datatypes, including numeric and alphanumeric formats, text, images, video, voice, speech, graphics, and also their mixed representations. Efficient management of such high-dimensional very large databases also influence the performance of data mining systems. Data Compression technologies can play a significant role. It is also important that special multimedia data compression techniques are explored especially suitable for data mining applications. With the completion of the Human Genome Project, we have access to large databases of biological information. Proper analysis of such huge data, involving decoding of genes in the DNA and the three-dimensional protein structures, holds immense promise in Bioinformatics. The applicability of data mining in this domain cannot be denied, given the lifesaving prospects of effective drug design. This is also of practical interest to the pharmaceutical industry. There are 10 chapters in the book. The first chapter provides an introduction to the basics of data mining and outlines its major functions and applications. This is followed in the second chapter by a discussion on soft computing and its different tools, including fuzzy sets, artificial neural networks, genetic algorithms, wavelet transforms, rough sets, and their hybridizations, along with their roles in data mining. Chapter 3 introduces the readers to the fundamentals of multimedia data compression and some popular algorithms for data compression. in Chapter 4 discuss the principles of string matching and some classical algorithms. Chapters 5 to 8 concentrate on classification, clustering, and rule mining. In each of these topics, in addition to the classical discussions that are usually available in the books currently in the market, we strive to incorporate new algorithms and results based on soft computing and advanced signal processing techniques with recent developments. In Chapter 9 deal with multimedia data mining. In Chapter 10 introduce the readers to issues from Bioinformatics.