找尋序列間關聯法則之研究
Other Title
A study on mining inter-sequence association rules
Date Issued
2004
Date
2004
Author(s)
DOI
922213E002066
Abstract
There are many algorithms proposed
to find sequential patterns in sequence
databases where a transaction contains a
sequence. Previously proposed algorithms
treat each sequence as an independent one.
This kind of mining belongs to
intra-sequence patterns mining, because
all the patterns found just describe
characteristics within a sequence. We
would like to go further to investigate
relationships between sequential patterns
in different sequences, called
inter-sequence association rules mining.
To the best of our knowledge, there are no
data mining techniques specially designed
to analyze the inter-sequence association
rules. Mining inter-sequence association
rules is used in many application areas.
We can use inter-sequence association
rules to analyze web page traversal,
telecommunication, disease symptoms,
weather changes, stock movements, DNA
sequences, and etc.
Therefore, in this project, we
proposed an algorithm to mine
inter-sequence association rules. First, we
use the PrefixSpan algorithm to find all
sequential patterns, and then we use a
level-wise method to check if an
sequence-set is large. We use a time point
list to collect all the time points at which
sequential patterns occur. Then, we divide
time point lists into several groups, and
store them in buckets, called L-buckets.
Since our proposed algorithm uses L-buckets
and time point lists to accelerate the process
of support counting, our proposed algorithm
outperforms the Apriori-like algorithm.
Subjects
Data mining
Association rules
intra-sequence association rules
Inter-sequence association rules
Publisher
臺北市：國立臺灣大學資訊管理學系暨研究所
Type
other
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