Performance Analysis: Apriori Algorithm with Regression in Weka Tool

This report presents an experimental analysis of the Apriori algorithm and regression techniques implemented within the Weka tool. The study focuses on optimizing execution time when generating frequent patterns, strong rules, and maximal rules. The implementation uses synthetic and real datasets, including a supermarket dataset, to evaluate the performance of the algorithms under varying support and confidence levels. The report includes implementation snapshots, result tables, and graphs comparing the execution times of Apriori and improved Apriori algorithms, along with Apriori with regression. Key findings highlight the impact of different support and confidence values on the execution time, with a specific emphasis on how linear regression can reduce the execution time for item sets with a predicted confidence of zero. Additionally, the report touches upon big data concepts like clustering and association rule mining, with a focus on minimizing costs associated with big data processing.

CHAPTER 1
EXPERIMENTAL RESULTS
5.1 IMPLEMENTATION OF PROPOSED WORK
In our thesis a weka tool of apriori and apriori algorithm with regression technique has been
actualized in weka tool to analyze the execution time of generating the frequent pattern,
strong rules, close rules and maximal. With the help of this application, we can easily find
the time which has taken to discover the frequent designs through apriori algorithm as well
as apriori with regression technique, in this weka tool we have found the frequent patterns
on dynamic value of support and confidence. By this work we can easily find out which
algorithm takes less execution time.
5.1.1 WEKA TOOL
Weka tool 4.5 was discharged on 15 August 2012; an arrangement of new or enhanced
features were included into this version. The Weka tool 4.5 will also run on Windows
Vista or later. The Weka tool 4.5 uses Common Language Runtime 4.0, with some
additional runtime features.
. Weka tool 4.5 is supported on Windows Vista, Server 2008, 7, Server 2008 R2, 8, Server
2012, 8.1 and Server 2012 R2. Applications utilizing Weka tool 4.5 will likewise keep
running on computers with Weka tool 4.6 installed, which supports additional operating
systems.
.NET Weka tool is available for building Metro-style apps using C# or Visual Basic.
CORE FEATURES
 Ability to restrict to what extent the regular expression engine will endeavor to
determine a regular articulation before it times out.
 Ability to characterize the culture for an application domain.
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 Console bolster for Unicode (UTF-16) encoding.
 Support for forming of social string requesting and comparison data. Better
execution while recovering resources.
 Native help for Zip compression (previous versions supported the compression
algorithm, but not the archive format).
 Ability to customize a reflection context to override default reflection behavior through
the Custom Reflection Context class.
 New asynchronous elements were added to the C# and Visual Basic languages. These
components include a task-based model for performing offbeat
operations, implementing futures and promises.
5.1.2 IMPLEMENTATION SNAPSHOT
 We will create the weka tool frame work with the help of regression and apriori
calculation to design the basic generating frequent use of data set values.
 In this frame work we will define some selected item set for implementation of
synthesis data set. In this, we compare the values of apriori and regression on different
confidence and support values.
Fig.5.1 Regression implemented for optimization weka snap shot
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Fig 5.1.1 snap shot of regression implemented in weka tool for time consuming.
Fig. 5.2 Snap Shot of Time Optimization through Apriori on support and confidence
Weka snap shot
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Fig. 5.3 Snap Shot of Time Optimization through improve Apriori support and
confidence using weka snap shot
.
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5.5 Snap Shot of Time Optimization through Apriori with regression on same support
and confidence
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Fig. 5.2 Snap Shot of Time Optimization through Apriori on help and certainty
Weka depiction with data set upload
Super market data set upload in weka tool for analyzing
5.2 DATASET OF THE PROPOSED APPROACH
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Remembering the true objective to assess the three proposed approaches, only single class of
dataset is utilized. They are,
 Synthetic dataset
 Supermarket dataset
 Real dataset
 Synthetic Dataset
Synthetic databases were created utilizing the MS-EXCEL. The data impersonates the
exchanges in a retailing domain. The execution of the algorithms is shown for manufactured
datasets. The accompanying datasets were produced with the true goal of assessing the three
proposed approaches,
 super market data set
 We have taken the data set of Super-market, in which there are 106 attributes
and 4627 instances.
 We can convert the real database into synthetic database on the basis of apriori
algorithm format given by a, b………..z values.
 Example:
a=bread
b= butter and so on.
 REAL DATASET
Association Rule mining likewise assumes significant part in finding the frequent example
from Real databases, overview data from Real research, data about the items and frequent
patterns, data containing, data concerning. This can confidence basic leadership concerning
the choice of items to be developed in a specific way in less time consuming.
5.2.1 ASSESSMENT PARAMETERS
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The execution of the proposed Market Basket methodology is assessed utilizing the
parameter like,
 Execution time
Execution time
Execution time demonstrates the time occupied by the standard Apriori procedure and
regression strategies to execute the procedure in all datasets. The technique which takes less
time in execution is best. This parameter is critical in Market Basket Investigation as
through this one can arrive at a decision about the relative execution of the proposed
methods. These performance values cause changes in the advancement of Super Market.
Assessment Parameters
The execution of the proposed Market Basket Investigation methodologies are assessed
utilizing the parameters like,
 Accuracy,
 Area Under the Curve :
 Sensitivity and
 Specificity
 Execution time
5.3 RESULT ANALYSIS
In this section, we have examined about the results like tables, graphs on different types of
support and confidence estimations of proposed work in different ways.
5.3.1 SIMULATION PARAMETERS
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1. In this table, there are distinctive time of execution (in sec) of both algorithm i.e., apriori
calculation and apriori with regression technique on different support count and different
confidence.
Results snap shots
Table 5.1 Comparison of Time Execution in Apriori and Improve Apriori with
supportand different Confidence using weka tool
Algo Sec
Apriori Algo 115
Improve apriori 76
Support Count
Min_sup=0.15
Confidence
Min_conf=0.9
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Size of
large
item set
L(1) L(2) L(3) L(4) L(5)
Apriori 41 347 855 617 105
Improved
apriori
34 173 226 96 10

Min_sup=0.15 Min_conf=0.9
Fig. 5.6 Time execution with different support and different confidence.
In this result analysis we will create the graph based on different support and different
confidence rules. As well as we have used the dot net frame work for implementation of this
time execution result. We analysis the linear regression technique reduced the time of
execution of those item set values whose confidence to be predict should be zero.
Time of execution in sec Sec
Aprioiri algorithm 115
Improve aprioiri 76
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Fig.5.7 Time optimization of Apriori with Improve Apriori in sec
Table 5.2 Comparison of Time Execution in Apriori and improve apriori with different
support and same Confidence
Algo Sec
Apriori Algo 102
Apriori Imrove
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Support Count
Min_sup=0.16
Confidence
Min_conf
=0.9
Size of large item-
sets
L(1) L(2) L(3) L
Apriori 38 322 707 4
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Improved Apriori 31 148 78 5
Fig. 5.8 Time execution with diverse support and same confidence.
In this result analysis we will create the graph in light of different support and same
confidence rules. And in addition we have used the dot net frame work for implementation
of this time execution result. We analysis the linear regression technique reduced the time of
execution of individuals item set values whose confidence to be predict should be zero.
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Fig.5.9 Time optimization of Apriori with Improve on different support and same
confidence
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