Multidimensional Sequential Pattern Mining

Data mining is the task of discovering interesting patterns from large amounts of data. There are many data mining tasks, such as classification, clustering, association rule mining, and sequential pattern mining. Sequential pattern mining is the process of finding the relationships between occurrences of sequential events, to find if there exists any specific order of the occurrences. It is a data mining task which finds the set of frequent items in sequence database. It is applicable in a wide range of applications since many types of data sets are in a time related format. Besides mining sequential patterns in a single dimension, mining multidimensional sequential patterns can give us more informative and useful patterns. Due to the huge increase in data volume and also quite large search space, efficient solutions for finding patterns in multidimensional sequence data are nowadays very important. In this paper, we discuss about sequential pattern mining, sequential pattern, methods used in sequential pattern mining and we will see how sequential pattern mining is not applicable for mining item set from multidimensional data. And why multidimensional pattern mining is necessary.