Efficient Approach to Discover Interval-Based Sequential Patterns

Abstract

In most of the sequential pattern mining methodology they have concentrated only on time point base event data. But some research efforts have detailed the mining patterns from time interval based event data. In many application most of the events are occurred at time interval based event not a point based interval for example patient affected by the certain time period. Our goal is to mine the frequently occurred sequential patterns in the database. In this study we have introduced a new algorithm namely KPrefixspan by modifying the TPrefixspan algorithm to overcome the demerits of that algorithm. Here new approach called refined database can reduce the scanning time extremely since the unsupported events are removed at each projection also result of the sequential pattern is extremely precise. Experiments constructed for synthetic datasets. From the experimental results we reduced the running time almost 60% and also reduce the memory usage almost 25% when compared to the existing TPrefixspan algorithm.