26,675 research outputs found
OSSM: Ordered Sequence set mining for maximal length frequent sequences
The process of finding sequential rules is an indispensable in frequent sequence mining. Generally, in sequence mining algorithms, suitable methodologies like a bottom2013;up approach will be used for creating large sequences from tiny patterns. This paper proposed on an algorithm that uses a hybrid two-way (bottom-up and top-down) approach for mining maximal length sequences. The model proposed is opting to bottom-up approach called 201C;Concurrent Edge Prevision and Rear Edge Pruning (CE
SEQUEST: Mining frequent subsequences using DMA strips
Sequential patterns exist in data such as DNA string databases, occurrences of recurrent illness, etc. In this study, we present an algorithm, SEQUEST, to mine frequent subsequences from sequential patterns. The challenges of mining a very large database of sequences is computationally expensive and require large memory space. SEQUEST uses a Direct Memory Access Strips (DMA-Strips) structure to efficiently generate candidate subsequences. DMA-Strips structure provides direct access to each item to be manipulated and thus is optimized for speed and space performance. In addition, the proposed technique uses a hybrid principle of frequency counting by the vertical join approach and candidate generation by structure guided method. The structure guided method is adapted from the TMG approach used for enumerating subtrees in our previous work [8]. Experiments utilizing very large databases of sequences which compare our technique with the existing technique, PLWAP [4], demonstrate the effectiveness of our proposed technique
Discovering unbounded episodes in sequential data
One basic goal in the analysis of time-series data is
to find frequent interesting episodes, i.e, collections
of events occurring frequently together in the input sequence.
Most widely-known work decide the interestingness of an episode from a
fixed user-specified window width or interval, that bounds the
subsequent sequential association rules.
We present in this paper, a more intuitive definition that
allows, in turn, interesting episodes to grow during the mining without any
user-specified help. A convenient algorithm to
efficiently discover the proposed unbounded episodes is also implemented.
Experimental results confirm that our approach results useful
and advantageous.Postprint (published version
- …