Covering based rough clustering of sequential data

Rough set approach is a very useful tool to handle the unclear and ambiguous data. In this approach, a set´s boundary region is used to express the incorrectness. Based on an equivalence relation, we can have upper and lower approximations for rough set. As rough sets make use of the equivalence relation property, they remain rigid. Rough set theory becomes a time consuming process because we need to find all the equivalence classes. It is unreliable and inefficient for real time applications where the data sets may be very large. In this paper, we provide a solution to this problem with covering based rough set approach. Covering based rough set is an extension of rough set approach in which the equivalence relation has been relaxed. This method is based on coverings rather than partitions. This makes it more flexible than rough sets and it is more convenient for dealing with complex applications. The purpose of covering based rough set is to get more number of overlapping. We uses covering based similarity measure which gives better results as compared to rough set which uses set and sequence similarity measure.