Mining Temporal Patterns for Interval-Based and Point-Based Events Abstract: Preceding research on mining sequential patterns mainly focused on determining patterns from point-based event data and interval–based event data, where a pair of time values is associated with each event. Since many areas of research includes data on a snapshot of time points as well as time intervals, it is necessary to define a new temporal pattern. In this work, based on the existing thirteen temporal relationships, a new altenative of temporal pattern is defined for interval based as well as point–based event data. Then, a hybrid pattern mining technique is proposed.Experimental results show that the extensiveness and precision of the proposed hybrid technique are more powerful than the existing algorithm. Existing System: Existing System mainly absorbed on discovering patterns from interval events has attracted considerable efforts due to its widespread applications. Sequential pattern mining is an essential data mining technique with broad applications. Several algorithms exhibit excellent performance in discovering sequential patterns from point-based data. However, in various real-world scenarios, some events intrinsically persist for periods of time. The data are typically a sequence of intervals with both start and finish times, such as, clinical data, appliance usage data,and library lending data, to name a few. Disadvantages: The pairwise relation between two time intervals is complex . It may increase candidate generation and the workload for counting the support of candidate sequence . Subsequently many areas of investigation includes data on a snapshot of time points as well as time intervals, it is necessary to define a new temporal pattern. Proposed System: In this System, we progress a novel algorithm, Play to play Mining Technique, to efficiently discover two types of interval-based sequential patterns. Some Trimming techniques are proposed to further reduce the search space of the mining process. Experimental studies show that proposed algorithm is efficient and scalable. Furthermore, we apply proposed method to real datasets to demonstrate the feasibility of discussed patterns. Advantages: To reduce the search space and avoid off-putting processes. This proposed algorithms are both efficient and accessible, and outpace the state-of-the-art algorithms. References: R. Agrawal and R. Srikant, “Mining Sequential Patterns,” IEEE ICDE’95, pp. 3-14, 1995. J. Allen, "Maintaining Knowledge about Temporal Intervals,” Communications of ACM, vol.26, issue 11, pp.832-843, 1983. P. Papapetrou, G. Kollios, S. Sclaroff, and D. Gunopulos, “Discovering frequent arrangements of temporal intervals,” IEEE ICDM’05,pp.354361,2005. D. Patel, W. Hsu and M. Lee, “Mining Relationships Among Intervalbased Events for Classification,” ACM SIGMOD’08, pp. 393-404, 2008. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.C.Hsu, “PrefixSpan: Mining Sequential Patterns Efficiently by PrefixProjected Pattern Growth,” IEEE ICDE’01, pp. 215-224, 2001. E. Winarko and J.F Roddick, “ARMADA-An algorithm for discovering richer relative temporal association rules from interval-based data,” Data& Knowledge Engineering, 63(1), pp. 76-90, 2007. S. Wu and Y. Chen, “Mining Nonambiguous Temporal Patterns for IntervalBased Events,” IEEE Transactions on Knowledge and Data Engineering, 19(6), pp. 742-758,2007.