Mining Frequent Max and Closed Sequential Patterns

Although frequent sequential pattern mining has an important role in many data mining tasks, however, it often generates a large number of sequential patterns, which reduces its efficiency and effectiveness. For many applications mining all the frequent sequential patterns is not necessary, and mining frequent Max, or Closed sequential patterns will provide the same amount of information. Comparing to frequent sequential pattern mining, frequent Max, or Closed sequential pattern mining generates less number of patterns, and therefore improves the efficiency and effectiveness of these tasks. This thesis first gives a formal definition for frequent Max, and Closed sequential pattern mining problem, and then proposes two efficient programs MaxSequence, and Closedsequence to solve these problems. Finally it compares the results, and performance of these programs with two brute force programs designed to solve the same problems.