Download Document

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Nonlinear dimensionality reduction wikipedia, lookup

Mining Temporal Patterns for Interval-Based and Point-Based
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
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
 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.
 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
Proposed System:
 In this System, we progress a novel algorithm, Play to play Mining
Technique, to efficiently discover two types of interval-based sequential
 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.
 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.
 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.