An incremental mining algorithm for maintaining sequential patterns using pre-large sequences

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International Journal of Modern Trends in Engineering
and Research
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@IJMTER-2014, All rights Reserved 386
e-ISSN: 2349-9745
p-ISSN: 2393-8161
An incremental mining algorithm for maintaining sequential patterns
using pre-large sequences
Miss. Radhika Jagzap1
, Prof. N. G. Pardeshi2
1,2
Computer Department, SRES College of Engineering, Kopargaon,
Abstract— Mining useful information and helpful knowledge from large databases has evolved into
an important research area in recent years. Among the classes of knowledge derived, finding
sequential patterns in temporal transaction databases is very important since it can help model
customer behavior. In the past, researchers usually assumed databases were static to simplify data-
mining problems. In real-world applications, new transactions may be added into databases
frequently. Designing an efficient and effective mining algorithm that can maintain sequential
patterns as a database grows is thus important. In this paper, we propose a novel incremental mining
algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce
the need for rescanning original databases.
Keywords- Data mining, Sequential pattern, Large sequence, Pre-large sequence, Incremental
mining
I. INTRODUCTION
What is sequential pattern mining? Sequential pattern mining is nothing but mining of frequently
occurring ordered events or subsequences as patterns. For example if a customer purchases Digital
camera he may purchase the color printer. The areas where sequential mining can be used are
customer shopping sequences, web access patterns analysis, weather prediction, production,
biological sequences and network intrusion detection [2].
The rapid development of computer technology, especially increased capacities and
decreased costs of storage media, has led businesses to store huge amounts of external and internal
information in large databases at low cost. Mining useful information and helpful knowledge from
these large databases has thus evolved into an important research area [3]. Years of effort in data
mining has produced a variety of efficient techniques. Among them, finding sequential patterns in
temporal transaction databases is important since it allows modeling of customer behavior [2] [1].
Mining sequential patterns was first proposed by [2], and is a non-trivial task. It attempts to find
customer purchase sequences and to predict whether there is a high probability that when customers
buy some products, they will buy some other products in later transactions. For example, a sequential
pattern for a video shop may be formed when a customer buys a television in one transaction, he then
buys a video recorder in a later transaction. Note that the transaction sequences need not be
consecutive. Although customer behavior models can be efficiently extracted by the mining
algorithm in [2] to assist managers in making correct and effective decisions, the sequential patterns
discovered may become invalid when new customer sequences occur.
1.1 Literature Survey
Recently, some researchers have strived to develop incremental mining algorithms for
maintaining association rules such as.

International Journal of Modern Trends in Engineering and Research (IJMTER)
Volume 01, Issue 05, [November - 2014] e-ISSN: 2349-9745, p-ISSN: 2393-8161
FUP algorithm proposed by [3] in this paper Weighted Frequent Pattern Mining (WFPM) has
brought the notion of the weight of the items into the Frequent Pattern mining algorithms. WFPM is
practically much efficient than the frequent pattern mining. Several Weighted Frequent Pattern
Mining methods have been used. However, they do not deal with the interactive and incremental
database. A IWFPTWU algorithm has been proposed to allow the users to decide the level of interest
and provides the direction for mining the interesting patterns. The Incremental Weighted Frequent
Patterns based on Transaction Weighted Utility (IWFPTWU) considers both the weight and the
frequency of the item. The IWFPTWU arranges the items in a decreasing order of the Transaction
Weighted Utilization (weighted Support). This makes uses of a single scan of the database for the
construction of the IWFPTWU tree.
The incremental mining algorithm with pre-large itemsets proposed [4][5]. In this paper, an
existing incremental algorithm, Probability-based incremental association rule discovery is used. The
previous algorithm, probability-based incremental association rule discovery algorithm uses principle
of Bernoulli trials to find frequent and expected frequent k-itemsets. The set of frequent and expected
frequent k-itemsets are determined from a candidate k-itemsets. Generating and testing the set of
candidate is a time-consuming step in the algorithm. To reduce the number of candidates 2-itemset
that need to repeatedly scan the database and check a large set of candidate, our paper is utilizing a
hash technique for the generation of the candidate 2-itemset, especially for the frequent and expected
frequent 2-itemsets, to improve the performance of probability-based algorithm. Thus, the algorithm
can reduce not only a number of times to scan an original database but also the number of candidate
itemsets to generate frequent and expected frequent 2 itemsets. As a result, the algorithm has
execution time faster than the previous methods. This paper also conducts simulation experiments to
show the performance of the proposed algorithm. The simulation results show that the proposed
algorithm has a good performance.
The common idea in these approaches is that previously mined information should be utilized
as much as possible to reduce maintenance costs. Intermediate results, such as large itemsets, are
kept and checked against newly added transactions, thus saving much computation time for
maintenance, although original databases may still need to be rescanned. Studies on maintaining
sequential patterns are relatively rare compared to those on maintaining association rules. Lin and
Lee proposed the FASTUP algorithm to maintain sequential patterns by extending the FUP
algorithm [1]. Their approach works well except when newly coming candidate sequences are not
large in the original database. If this occurs frequently, the performance of the FASTUP algorithm
will correspondingly decrease. In this seminar, thus an attempt to develop a novel and efficient
incremental mining algorithm capable of updating sequential patterns based on the concept of pre-
large sequences is done.
A pre-large sequence is not truly large, but nearly large. A lower support threshold and an upper
support threshold are used to realize this concept. Pre-large sequences act like buffers and are used to
reduce the movement of sequences directly from large to small and vice versa during the incremental
mining process. A safety bound for newly added customer sequences is derived within which
rescanning the original database can be efficiently reduced and maintenance costs can also be greatly
reduced. The safety bound also increases monotonically along with increases in database size. Thus,
the proposed algorithm becomes increasingly efficient as the database grows. This characteristic is
especially useful for real-world applications.

II. PROPOSED SYSTEM
Figure 1. Basic Architecture
The system works as follows:
I. The Datasets are taken as input.
II. Preprocessing is the first step in data mining in which data is cleaned.
III. Apply the apriori algorithm to generate the large and pre-large item set. Apriori takes upper
and lower support as input.
IV. Where support is probability of getting item set from given dataset and confidence is
percentage of one item with respect to another in given dataset.
V. Check for the safety bounds if it is within the bound then it will generate the result directly
else the incremental mining algorithm is being applied and then the result is generated.
2.1 Extending the Concept of Pre-large Item- sets to Sequential Patterns:
Maintaining sequential patterns is much harder than maintaining association rules since the
former must consider both itemsets and sequences. In this seminar, an attempt to extend the concept
of pre-large itemsets to maintenance of sequential patterns is done. The prelarge concept is used here
to postpone original small sequences directly becoming large and vice versa when new transactions
are added. A safety bound derived from the lower and upper thresholds determines when rescanning
the original database is needed. When new transactions are added to a database, they can be divided
into two classes:
Class 1: new transactions by old customers already in the original database;
Class 2: new transactions by new customers not already in the original database.
Considering the old customer sequences in terms of the two support thresholds, the newly
merged customer sequences may fall into the following three cases illustrated in figure 2.

Figure 2. Three cases arising from adding new transaction to existing databases.
Case 1 may remove existing large sequences, and cases 2 and 3 may add new large
sequences. If we retain all large and pre-large sequences with their counts in the original database,
then cases 1 and 2 can be easily handled. Also, in the maintenance phase, the ratio of newly added
customer sequences to original customer sequences is usually very small. This is more apparent
when the database is growing larger. A sequence in case 3 cannot possibly be large for the entire
updated database as long as the number of newly added customer sequences is small compared to the
number of customer sequences in the original database.
III. IMPLEMENTATION DETAILS
Before applying the incremental mining algorithm the Apriori algorithm is applied in order to
determine whether the given data exceeds the safety bound. If it exceeds the safety bound then the
incremental mining algorithm is applied.
Following are the steps of apriori algorithm:
There are total five phases included in this approach.
1. In the first phase, transactions are sorted first using customer ID as the major key and then
using transaction time as the minor key. This phase thus converts the original transactions
into customer sequences.
2. In the second phase, large itemsets are found in customer sequences by comparing their
counts with the predefined support parameter α. This phase is similar to the process of mining
association rules. Note that when an itemset occurs more than once in a customer sequence, it
is counted once for this customer sequence.
3. In the third phase, large itemsets are mapped to contiguous integers and the original customer
sequences are transferred to the mapped integer sequences.
4. In the fourth phase, the integer sequences are examined for finding large sequences.
5. In the fifth phase, maximally large sequences are then derived and output to users.
3.1 Incremental Mining Algorithm:
The proposed an incremental mining algorithm is based on the concept of pre-large itemsets
to reduce the amount of rescanning of original databases required whenever new transactions are
added (Hong et al., 2000, 2001)[6][7]. A pre-large itemset is not truly large, but promises to be large
in the future. A lower support threshold and an upper support threshold are used to realize this
concept. The upper support threshold is the same as the minimum support used in conventional
mining algorithms. The support ratio of an itemset must be larger than the upper support threshold in
order to be considered large. On the other hand, the lower support threshold defines the lowest
support ratio for an itemset to be treated a pre-large. An itemset with a support ratio below the lower

threshold is thought of as a small itemset. Pre-large itemsets act like buffers and are used to reduce
the movement of itemsets directly from large to small and vice versa in the incremental mining
process. Therefore, when few new transactions are added, the original small itemsets will at most
become pre-large and cannot become large, thus reducing the amount of rescanning necessary. A
safety bound for new transactions is derived from the upper and lower thresholds and from the size
of the database. This algorithm is described as follows:
Step 1: Retain all previously discovered large and pre-large itemsets with their counts.
Step 2: Scan newly inserted transactions to generate candidate 1- itemsets with counts.
Step 3: Set k = 1, where k is used to record the number of items currently being processed.
Step 4: Partition all candidate k-itemsets as follows.
Case 1: A candidate k-itemset is among the previous large 1- itemsets.
Case 2: A candidate k-itemset is among the previous pre-large itemsets.
Case 3: A candidate k-itemset is among the original small itemsets.
Step 5: Calculate a new count for each itemset in cases 1 and 2 by adding its current count and
previous count together; prune the itemsets with new support ratios smaller than the lower support
threshold.
Step 6: Rescan the original database if the accumulative amount of new transactions exceeds the
safety threshold.
Step 7: Generate candidate k + 1-itemsets from updated large and pre-large k-itemsets, and then go
to step 3 until they are null.
The above algorithm, like the FUP algorithm, retains previously mined information, focuses
on newly added transactions, and further reduces the computation time required to maintain large
itemsets in the entire database. The algorithm can further reduce the number of rescans of the
original database as long as the accumulative amount of new transactions does not exceed the safety
bound.
Example:
Table 1: Product Id with name
Prod_id Prod_name
1 Milk
2 Bread
3 Cheese
4 Tea Powder
5 Sugar
6 Tooth Paste
7 Tooth Brush

Table 2: Transaction
Trans_id Items
1 1,2
2 2,3,4,7
3 2,3,5
4 1,2,4,7
5 1,3
6 2,3,5
7 1,3,6
8 1,2,3
9 1,2,3
Table 3: Product Id with support value
By applying Min support=2, Max support=4,
Table 4: Candidate-1 Sequence
Prod_id Count
1 6
2 7
3 7
4 2
5 2
6 1
7 2
Large Itemset Pre-Large
Itemset
Prod_id Count Prod_id Count
1 6 4 2
2 7 5 2
3 7 7 2
Itemset
Prod_id count Prod_id Count
1,2 4 4,7 2
1,3 4 2,4 2
3,2 5 2,5 2

Table 7: Two New transactions are added
By applying Min Support=3, Max Support=5,
IV. CONCLUSION
In this paper, a novel incremental mining algorithm capable of maintaining sequential
patterns based on the concept of pre-large sequences has been proposed. Pre-large sequences act like
buffers to postpone originally small sequences directly becoming large and vice versa, when new
transactions are inserted into existing databases. It also proves that when small numbers of new
Pre-Large Itemset
Prod_id Count
1,2,3 2
2,3,5 2
Trans_id Items
10 2,3,4,5
11 1,2,3
Itemset
1 7 4 3
2 9 5 3
3 8
Itemset
1,2 5 2,4 3
1,3 5 2,5 3
3,2 7
Pre-Large Itemset
Prod_id Count
1,2,3 3
2,3,5 3

transactions are inserted, an originally small sequence will at most become pre large, and never large.
The number of rescans of original databases can thus be reduced.
REFERENCES
[1] Lin, M. Y. & Lee, S. Y., "Incremental update on sequential patterns in large databases", In The
10th IEEE international conference on tools with artificial intelligence, pp. 24-31, 1998.
[2] Agrawal, R., Srikant, R. ,"Mining sequential patterns", The Eleventh IEEE International
Conference on Data Engineering, 3-14, 1995.
[3] Chen, M. S., Han, J., Yu, P. S., "Data mining: An overview from a database perspective", IEEE
Transactions on Knowledge and Data Engineering, 866-883, 1996.
[4] Hong, T. P., Wang, C. Y., Tao, Y. H., "Incremental data mining based on two support
thresholds", In The fourth international conference on knowledge based intelligent engineering
systems and allied technologies, 2000.
[5] Hong, T. P., Wang, C. Y., Tao, Y. H., "A new incremental data mining algorithm using pre-large
itemsets", Intelligent Data Analysis, 5(2), 111-129, 2001.

An incremental mining algorithm for maintaining sequential patterns using pre-large sequences

An incremental mining algorithm for maintaining sequential patterns using pre-large sequences

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An incremental mining algorithm for maintaining sequential patterns using pre-large sequences