Data mining refers to a process on nontrivial extraction of implicit, previously unknown and potential useful information (such as knowledge rules, constraints, regularities) from data in databases. With the availability of inexpensive storage and the progress in data capture technology, many organizations have created ultra-large databases of business and scientific data, and this trend is expected to grow. Since the databases to be mined are likely to be very large (measured in terabytes and even petabytes), there is a critical need to investigate methods for parallel data mining techniques. Without parallelism, it is generally difficult for a single processor system to provide reasonable response time. In this chapter, we present a comprehensive survey of parallelism techniques for data mining. Parallel data mining offers new complexity as it incorporates techniques from parallel databases and parallel programming. Challenges that remain open for future research will also be presented.