In this paper, we have proposed a novel sequential mining method. The method is fast in comparison to existing method. Data mining, that is additionally cited as knowledge discovery in databases, has been recognized because the method of extracting non-trivial, implicit, antecedently unknown, and probably helpful data from knowledge in databases. The information employed in the mining method usually contains massive amounts of knowledge collected by computerized applications. As an example, bar-code readers in retail stores, digital sensors in scientific experiments, and alternative automation tools in engineering typically generate tremendous knowledge into databases in no time. Not to mention the natively computing- centric environments like internet access logs in net applications. These databases therefore work as rich and reliable sources for information generation and verification. Meanwhile, the massive databases give challenges for effective approaches for information discovery.

1. IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 08, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 170
Fast Sequential Rule Mining
Bharat Parmar1
Prof. Gajendra Singh Chandel2
1,2
Department of Computer Science
1,2
Sri Satya Sai Institute of Science and Technology, Indore, Bhopal, India
Abstract— In this paper, we have proposed a novel
sequential mining method. The method is fast in
comparison to existing method. Data mining, that is
additionally cited as knowledge discovery in databases,
has been recognized because the method of extracting
non-trivial, implicit, antecedently unknown, and probably
helpful data from knowledge in databases. The
information employed in the mining method usually
contains massive amounts of knowledge collected by
computerized applications. As an example, bar-code
readers in retail stores, digital sensors in scientific
experiments, and alternative automation tools in
engineering typically generate tremendous knowledge into
databases in no time. Not to mention the natively
computing- centric environments like internet access logs
in net applications. These databases therefore work as
rich and reliable sources for information generation
and verification. Meanwhile, the massive databases give
challenges for effective approaches for information
discovery.
Key words: simple current mirror, Low voltage current
mirror, level shifted Current mirror, Level shifted low
voltage current mirror, Dynamic range
I. SEQUENTIAL RULE MINING
The sequential pattern is a sequence of itemsets that
regularly occurred in an exceedingly specific order, all
items within the same itemset are supposed to have
constant transaction duration or inside a time-
gap. Sequential patterns indicate the correlation between
transactions whereas association rules represent intra-
transaction relationships.
Sequential pattern mining was initially introduced
by Agrawal and Srikant [1]. It's the process of extracting
certain sequential patterns whose support exceeds a
predefined lowest support threshold. Since the number of
sequences may be very massive, and users have completely
different interests and necessities, to induce the most
interesting sequential patterns sometimes a minimum
support is predefined by the users. By using the minimum
support we will prune out those sequential patterns of no
interest, consequently creating the mining method more
economical. Clearly a better support of a sequential
pattern is desired for additional helpful and interesting
sequential patterns. But some sequential patterns that don't
satisfy the support threshold are still fascinating. Yang,
Wang & Yu (2001) [3] introduced another metric
known as surprise to measure the powerfulness of
sequences. A sequence s is a stunning pattern if its
occurrence differs greatly from the expected occurrence,
once all items are treated equally. Within the surprise
metric the information gain was proposed to measure the
degree of surprise, as detailed by yang [3].
Sequential pattern mining is used in a very great
spectrum of areas. In machine biology, sequential pattern
mining is employed to analyses the mutation patterns of
various amino acids. Business organizations use
sequential pattern mining to check client behaviors.
Sequential pattern mining is additionally utilized in
system performance analysis and telecommunication
network analysis.
General algorithms for sequential pattern mining
are proposed recently. Most of the essential and earlier
algorithms for sequential pattern mining are based on
the Apriori property proposed by Srikant and Agrawal
[2]. The property states that any sub-pattern of a frequent
pattern should be frequent. Based on this heuristic, a
series of Apriori-like algorithms are proposed: AprioriAll,
Apriori Some, Dynamic some [2], GSP [4] and SPADE
[5]. Later, a series of information projection primarily
based algorithms were proposed , including
FreeSpan [6] and PrefixSpan [7]. By using the minimum
support we will prune out those sequential patterns of no
interest, consequently creating the mining method more
economical. Many closed sequence mining algorithmic
rule were additionally i n t r o d u c e d , l i k e C l o S p a n
[8] a n d T S P [ 9]. Most o f t h o s e projection-based
algorithms are explained within the next subsections.
II. PROPOSED SOLUTION
 A source database D.
 MST (Minimum Support Threshold).
 MCT (Minimum Confidence Threshold).
III. PROCEDURE
 We set the minimum support and scan the database
to get 1-itemsets. In this step, we also count each
item’s support by using compressed data structure,
i.e. head and body of the database. Here body of
the database contain itemset with their support and
arranges in the lexicographic order, i.e. sorted
order. The proposed method first scans the
sequential data base once & it count the support
single size frequent items. the algorithm then
arrange each pair of frequent elements in form of a
rule. Like for a pair {1,2}, it generates two rules as
: 1 => 2 & 2=>1. It also eliminates all infrequent
items.
 Then, the sequential support and sequential
confidence of every rule is calculated. All rules
having support and confidence greater than the
minimum threshold are valid rules.
 Then all the rules found in step 2 are expanded on
their left and right side as follows:
 Left-side growth is the method of
taking two rules X⇒Y and Z⇒Y,
wherever X and Z are itemsets of size n
sharing n-1 items, generate a larger rule
 X∪Z ⇒Y. Right-side expansion is the
process of taking two rules Y⇒X and
Y⇒Z, wherever X and Z are unit itemets

2. Fast Sequential Rule Mining
(IJSRD/Vol. 2/Issue 08/2014/041)
All rights reserved by www.ijsrd.com 171
of size n sharing n-1 items, to generate a
larger rule Y⇒X∪Z. These two
processes are applied recursively to seek
out all rules ranging from rule of size 1*1
(for example, rules of size 1*1 permits
finding rules of size 1*2 and rule of size
2*1).
IV. CONCLUSION
In this paper, we tend to conferred a completely unique
algorithm for mining sequential rules common to several
sequences. Unlike previous algorithms, it doesn't use a
generate-candidate-and-test approach. Instead, it uses a
pattern-growth approach for discovering valid rules
specified it will be rather more efficient and scalable.
It initial finds rules between 2 items and so
recursively grows them by scanning the database for
single items that would expand their left or right
components. we've evaluated the performance of our
algorithm by scrutiny it with CMRules algorithms.
Results show that our algorithm clearly outperforms
CMRules and incorporates a higher scalability.
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[1] Rakesh Agr a wa l , & Ramakrishnan
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