SEQUEST: Mining Frequent Subsequences
Using DMA-Strips
Author(s)
H. Tan, T. S. Dillon, F. Hadzic & E. Chang
Abstract
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.
Keywords: mining frequent subsequences, sequence, phylogenic tree, sequential
strips.
1 Introduction
Data mining strives to find frequent patterns in data [1–4, 12]. A task in data
mining is characterized by the type of data, structures and patterns to be found
[8, 9]. Advancements in data collection technologies have contributed to the high
volumes of sales data. Sales data typically consists of a transaction timestamp
and the list of items bought by customers. If one is interested in inter-transactional
patterns, sales data is a good example of a sequential pattern. Other
Keywords
mining frequent subsequences, sequence, phylogenic tree, sequential
strips.
Related Book
Other papers in this volume
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