Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Networks for Downlink

IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 4, 2013 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 870
Abstract— The analysis of observational data sets to find
unsuspected relationships and to summarize the data in
novel ways that are both understandable and useful to the
data owner. To find the association rules among the
transactional dataset is the main problem of frequent
itemset mining. Many techniques have been developed to
increase the efficiency of mining frequent itemsets. In this
paper, we denote a new method for generating frequent
itemsets using frequent itemset tree (FI-tree). Also we
describe the example of new method and its result analysis
using wine dataset. Our method execution time is better
compare to SaM method.
Keywords: Itemsets, Mine, Tree, SaM.
I. INTRODUCTION
The idea is to seek for something called knowledge,
which means regularities, rule and structure hidden in the
data. This activity is a subfield of computer science
called knowledge discovery or sometimes data mining.
This knowledge will help in making decisions and
conclusions that lead to value creation for both the user and
the owner of the data. For instance, the purchase
information collected by a supermarket chain may help the
supermarket to adjust product offering and availability to
better suit the needs of its customers. A credit card
company familiar with the purchase history of its
customers can detect when a credit card has been stolen and
used to buy goods or services that the customer would be
unlikely to buy himself. Using location tracking
technologies, a cell phone service provider can offer map-
based services such as navigation or search of nearby
restaurants. Moreover, bus and train companies can use
recorded passenger data to help plan bus services to run
more often where needed.
Apriori algorithm is quite successful for market
based analysis in which transactions are large but frequent
items generated is small in number. Apriori algorithm is used
as a recommendation engine in an E-commerce system.
Based on each visitor‘s purchase history the system
recommends related, potentially interesting, products. It is
also used as basis for a CRM system as it allows the
company itself to follow-up on customer‘s purchases and to
recommend other products by e-mail. To analyze the
huge amount of data thereby exploiting the consumer
behavior and make the correct decision leading to
competitive edge over rivals1
. Also Sequential association
rule mining is one of the possible methods to analysis of data
used by frequent itemsets2
.
A dataset is a set D of observations made over a
set of attributes A More specifically, each observation in
D is a Vector of (measured) values observed
simultaneously for the set of attributes A. As a whole, D
can be viewed as a matrix of n rows of observation vectors
and m columns with attributes as headers. An observation
can also be referred to as a data point. We denote a single
attribute by a capital letter from the beginning of the
alphabet, that is, by A, B, C . . ., and so on. An
observation is denoted by the row vector t. Given an
attribute A and a row t, we denote the value of attribute A
on row t by t(A). Depending on the type of attribute A,
the value of t(A) may be either
BINARY, THAT IS T(A) ∈ {0, 1}, OR NUMERICAL, THAT IS,
T(A) ∈ R.
Binary valued attributes are o f t e n called
items. Items are attributes that can be either present or
absent at the moment of observation. the entire set of items
is denoted by i. for datasets consisting of only binary
attributes we have i = a, and by convention, a row t may
be expressed as a subset of the universe of all attributes,
i.e., t ⊆ i, containing those binary attributes
In this paper, we denote the overview of frequent
itemset mining algorithms. Next, we denote a new
method for generating frequent itemsets using frequent
itemset tree (FI- tree). Also we describe the example of
new method and its result analysis. Finally, conclude and
future scope of the paper.
A. Define a Problem:
Mining of frequent itemset is acknowledged in the data
mining field because of its broad applications in
mining association rules, correlations, and graph pattern
constraint based on frequent patterns, sequential patterns,
and many other data mining tasks. Efficient algorithms
for mining
Frequent itemsets are crucial for mining association
rules as well as for many other data mining tasks. The
major challenge found in frequent pattern mining is a large
number of result patterns. As the minimum threshold
becomes lower, an exponentially large number of itemsets
are generated. Therefore, pruning unimportant patterns can
be done effectively in mining process and that becomes
one of the main topics in frequent pattern mining.
Consequently, the main aim is to optimize the process of
finding patterns which should be efficient, scalable and can
detect the important patterns which can be used in
various ways3
.
B. Gap Analysis:
All the algorithms produce frequent itemsets on the basis
of minimum support. Apriori algorithm is quite successful
for market based analysis in which transactions are large
but frequent items generated is small in number4
. Vertical
Layout based algorithms claims to be faster than Apriori
A New Method to Mine Frequent Item Sets using Frequent Itemset Tree
Mr. Hardik S. Patel1
Prof. Jigar N. Patel2
1
P.G. Student 2
Internal Guide
1, 2
Alpha College of Engg. & Technology, Khatraj, Kalol.

A New Methodto Mine FrequentItemSetsusing FrequentItemset
(IJSRD/Vol. 1/Issue 4/2013/0016)
but require larger memory space then horizontal layout
based because they needs to load candidate, database and
TID list in main memory5
. For FP-Tree6
and H-mine7
,
performs better than all discussed above algorithms
because of no generation of candidate sets but the pointes
needed to store in memory require large memory space.
For PASCAL a l g o r i t h m f i n d s both frequent and
closed sets and it is 10 times as fast as Apriori but is only
practical when the pattern length is short8
. For FIAST
algorithm, tries to reduce I/O, space and time but
performance decreases for sparse datasets9
. For SaM
algorithm claims to be faster than all discussed above
algorithms but require preprocessing on database results in
execution time overhead10
. Therefore these algorithms are
not sufficient for mining the frequent itemsets for large
transactional database
C. Method of Frequent Itemset Mining:
In a large transactional database like retailer database it is
common that multiple items are selling or purchasing
simultaneously therefore the database surly contains
various transactions which contain same set of items. Thus
by taking advantage of these transactions trying to find out
the frequent itemsets and prune off the candidate itemsets
whose node count is lower than min support using their FI-
tree data structure without multiple database scan, results in
efficiently execution time.
The method is used FI-tree, there are nodes holding
frequent itemsets and transactions containing related
itemsets. The principles of intersection and union are
related to FI-tree. These principles are related to Frequent
Itemset tree. In Frequent Itemset tree, there are nodes
holding frequent itemsets and Tid containing related
itemsets. The presented new method for mining frequent
itemsets is a bottom-up level wise method that utilizes both
Item set space and transaction space.
In order to construct k-itemsets, frequent (k-1)-
itemsets are used. Their union is formed and for their
support count and intersection operation is employed
between the Tids of the itemsets.
Itemset {A} is in transactions with Tid 1, 4, 5, 8
and {B} is in transactions with Tid 1, 2, 4, 5, 8, i.e.
T(A)={1, 4, 5, 8} and T(B)={1, 2, 4, 5, 8}. The Itemset
{A, B} is the union of these two itemsets and
intersection principles is used to find the tids for {A, B}
as follows:
T(AB) = T(A) ∩ T(B) = {1, 4, 5, 8} ∩ {1, 2, 4, 5,
8} = {1, 4, 5, 8}
If the result is greater than minimum support, it
will be joined to frequent Itemset tree. If the result is lower
than minimum support, it will be pruned off. The more
detail steps as follows:
Database D, minimum support are input by user and some
or all frequent itemsets are output after execution of
method. Assume that the frequent 1-itemsets and
transaction sets, require no more memory that available
and also there is space for generating candidate 2-itemsets
from frequent 1-itemset. Scan database and find frequent
1-itemsets, at the same time obtain transaction sets,
which includes the Itemset. Generate candidate 2-itemsets
from frequent 1-itemset only. Prune off the candidate 2-
itemsets whose node count is lower than min support using
their Tidset. Now Frequent Itemset tree contains only
frequent 2-itemsets at the second level. Consequently,
for each frequent 3, 4,,,, n– itemset, based on node count
to approve the consistence of the Itemset.
Now F r e q u e n t I t e m s e t t r e e contains f r e q u e n t
3 - itemsets, frequent 4-itemsets.... frequent n-itemsets at
the third level, forth level.... nth
level respectively.
1) Example:
An example based on the database of retailer, D (table
1). There are six transactions in this database, that is,
|D|=6. Suppose the minimum support is 3. We use the
proposed new method for finding frequent itemsets in D,
based on the intersection and union operation using
below sample retailer transactional database (Table 1).
Table (1): Sample retailer transactional database
Database D, minimum support = 3 are input by user and
some frequent itemsets ({1}, {2}, {3}, {5}, {7}, {8}, {1,
3}, {1, 7}, {2, 3}, {2, 5}, {3, 7}, {1, 3, 7}).
After scanning a database put frequent 1-itemsets
(table 2) with the count of repetition and Tid containing
related itemsets. It is found that {4} and {6} itemsets,
which is not frequent 1-itemset and removed. Generate
candidate 2-itemsets from frequent 1- itemset only. Prune
off the candidate 2-itemsets whose node count is lower
than min support using their Tidset. Now FI-tree contains
only frequent 2-itemsets at the second level and shown
(Table 3) Find frequent 3-Itemset based on node count
to approve the consistence of the Itemset. Now FI-tree
contains frequent 3-itemsets at the third level. Also
frequent 3-itemsets shown (table 4).

A New Methodto Mine FrequentItemSetsusing FrequentItemset
(IJSRD/Vol. 1/Issue 4/2013/0016)
II. RESULT ANALYSIS
In our experiments, we select wine dataset with different
properties to prove the efficiency of the method. In
wine dataset, 178 numbers of records and 14 numbers of
columns (table 5). Wine dataset are used to test the new
method by different settings of support thresholds. The
SaM and new method are executed over the wine dataset.
The total execution time taken for executing the SaM this
method used wine dataset.
Files
Number of
Records
Number of
Columns
wine.data.txt 178 14
Table (5): Characteristics of dataset
Support (in %)
Total Execution time in second
SaM New Method
30 3.70 3.26
45 1.89 1.82
60 0.22 0.18
Table (6): SaM and New Method Execution Time using
wine dataset
Method (table 6) The total execution time for the new and
SaM methods large reduces with the increase in support
threshold from 30% to 60% for wine dataset. At 60%
support threshold, two methods nearly matches the
execution time. Our method takes less time as that
compared to SaM method (figure 1).
Fig (1): Execution time of wine dataset
III. CONCLUSION
By analytical study of the classical frequent itemset
mining algorithms such as, Apriori, Eclat, FP-growth,
Pascal, H-mine, Frequent Itemsets Algorithm for Similar
Transactions (FIAST) and Split and Merge (SaM) To find
out the advantages and disadvantages of these algorithms
using different parameters. The SaM method is better from
all above exiting methods.
We denote our new method for generating
frequent itemsets by using frequent itemset tree (FI-tree).
The analysis of total execution time for generating
frequent itemsets denoted with standard dataset wine.
Our method execution time is better compare to SaM
method. At 60% support threshold, two methods nearly
match the execution time.
We are use some constraints by user input for
reduce total execution time for mining frequent itemsets.
We are developing application based on this method.
REFERENCES
[1] Raorane A.A., Kulkarni R.V. and Jitkar B.D.,
Association Rule – Extracting Knowledge Using
Market Basket Analysis, Res. J. Recent Sci.,1(2), 19-27
(2012)
[2] Shrivastava Neeraj and Lodhi Singh Swati, Overview
of Non-redundant Association Rule Mining, Res. J.
Recent Sci., 1(2), 108-112 (2012)
[3] Pramod S., Vyas O.P., Survey on Frequent Item
set Mining Algorithms, In Proc. International Journal of
Computer Applications, 1(15), 86–91 (2010)
[4] Agrawal R. and Srikant R., Fast algorithms for
mining association rules, In Proc. Int’l Conf. Very
Large Data Bases (VLDB), 487–499 (1994)
[5] Borgelt C., Efficient Implementations of Apriori and
Eclat, In Proc. 1st IEEE ICDM Workshop on Frequent
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[6] Han J., Pei H. and Yin. Y., Mining Frequent
Patterns without Candidate Generation, In Proc. Conf.
on the Management of Data (2000)
[7] Yves Bastide, Rafik Taouil, Nicolas Pasquier,
Gerd Stumme, Lotfi Lakhal, Mining Frequent Patterns
with Counting Inference, In Proc.ACM SIGKDD, 66-
75 (2000)
[8] Pei. J., Han. J., Lu. H., Nishio. S., Tang. S. and Yang.
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Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Networks for Downlink

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