The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the fields of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. As a result, traditional adhoc mixtures of statistical techniques and data management tools are no longer adequate for analyzing this vast collection of data. Several domains where large volumes of data a restored in centralized or distributed databases includesapplications like in electronic commerce, bio- formatics, computer security, Web intelligence, intelligent learning database systems, finance, marketing, healthcare, telecommunications,and other fields. Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and effective manner. However,the data to be analyzed is imprecise and afficted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly conflicting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.