International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 7
RESEARCH ARTICLE OPEN ACCESS
Design and Development of Data Mining System to Analyze Cars
using Improved ID3 with TkNN Clustering Algorithm
M.Jayakameswaraiah1, Prof.S.Ramakrishna2
Ph.D. Research Scholar1, Professor2,
Department of Computer Science,
Sri Venkateswara University, Tirupati,
AP-India
ABSTRACT
Conventional way of business is a challenging in car market due to many competitors are there around the world for providing
competitive products. The car manufacturers categorizes the car users and have to invent a suitable car; the seller correctly
groups the buyers and he sells a right car; and the customers selects best car by analyzing more brands of cars with ‘N’ number
of sellers. These three cases they spent too much of time for analyzing old or statistical data for choosing a right product. Now a
day’s customers are required comfort and their loving brand & color. With the advent of the Internet and Data Mining
Algorithms has undoubtedly contributed to the shift of marketing focus. In this paper, we proposed Improved ID3 with TkNN
algorithm for best car market analysis. We have executed the same in WEKA Tool with Java code. We analyzed the graphical
performance analysis between TkNN and our novel improved ID3 with TkNN clustering algorithms with Classes to Clusters
evaluation purchase, safety, luggage booting, persons (seating capacity), doors, maintenance and buying attributes of customer’s
requirements for unacceptable/acceptable/good/very good ratings of a car to purchase.
Keywords: - ID3 Algorithm, KNN, Improved ID3 with TkNN Algorithm
I. INTRODUCTION
Economic growth of a country depends on
transportation as one constraint. Like many economic
activities that are intensive in the use of infrastructures, the
transport sector is an important component of the economy
impacting on development and the welfare of population. A
relation between the quantity and quality of transport
infrastructure and the level of economic development is
apparent. When transport systems are efficient, they provide
economic and social opportunities and benefits that result in
positive multipliers effects such as better accessibility to
markets, employment and additional investments.
A new business culture is developing today [4, 7, 24,
19]. Within it, the economics of customer relationships are
changing in fundamental ways, and companies are facing the
need to implement new solutions and strategies that address
these changes. The concepts of mass production and mass
marketing, first created during the Industrial Revolution, are
being supplanted by new ideas in which customer
relationships are the central business issue [1, 5]. Firms today
are concerned with increasing customer value through
analysis of the customer lifecycle. The tools and technologies
of data warehousing, data mining, and other customer
relationship techniques afford new opportunities for
businesses to act on the concepts of relationship marketing.
The old model of “design-build-sell” (a product-oriented
view) is being replaced by “sell-build-redesign” (a customer-
oriented view). It is a spiral model of software engineering
[26]. The traditional process of mass marketing is being
challenged by the new approach of one-to-one marketing. In
the traditional process, the marketing goal is to reach more
customers and expand the customer base [31, 11, 5]. But
given the high cost of acquiring new customers, it makes
better sense to conduct business with current customers. In so
doing, the marketing focus shifts away from the breadth of
customer base to the depth of each customer’s needs.
The lifecycle of a modern car comprises a multitude
of complex and interdependent tasks that start early during
the development phase, guide and advice the production
process and keep track of issues related to operating vehicles.
We will present examples of data mining applications from
all these three stages: development, production planning and
fault analysis. All contributions share the property that we use
(or extract) rule patterns to explain the domain under analysis
to the user. Rules (in form of association rules) are a well-
understood means of representing knowledge and data
dependencies. The inherent interdisciplinary character of the
automobile development and manufacturing process requires
models that are easily understood across application area
boundaries. The understanding of patterns can be greatly
enhanced by providing powerful visualization methods
alongside with the analysis tools.
The next section will briefly sketch the underlying
theoretical frameworks, after which we will present and
discuss successfully applied fault analysis, planning and
development methods, all of which have been rolled out to
production sites of two large automobile manufacturers.
Section 2 provides some notations needed for the rest of
the article. Section 3 discusses an approach based on
graphical models to assess and reveal potential fault patterns
inside vehicle data. Section 4 deals with the handling of
production planning. Section 5 discovers rules from time
series that are created by prototype simulations. Finally,
section 6 concludes the findings.
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 8
II. BACKGROUND
Selecting the suitable car is extremely tricky job if
parameters (color, comfort, seating capacity, maintenance,
price, and so on) are known otherwise it is difficult task. If the
customer knows these all things then also sometimes it is hard
to choose the right car.
The problem is unmanageable in the perspective of
manufacturer and seller, because they must work with
different categories of people [31]. Some people preferred
only high cost cars, some are low price with all features and
others are in between these classes. One more category of
people are only knowing information about different brands
but they never buys.
The need to increase the productivity of manufacturer,
raises the seller transactions and customer satisfy of the
selected car comforts. Comfort transportation is encourages
frequency of vehicle usage, it increases Economic growth as
well as it decreases wastage of time in journey. Car is the
symbol of comfortable.
If this system is available then there are no capabilities
required to assist with telecom expense management, i.e., the
administrator can find out the number of calls and text
messages used as well as cellular and WiFi data usage, both
for home and roaming networks [19].
We will now briefly discuss the notational underpinning
that is needed to present the ideas and results from the
industrial applications.
Graphical Models
As we have pointed out in the introduction, there are
dependencies and independencies that have to be taken into
account when reasoning in complex domains shall be
successful. Graphical models are appealing since they provide
a framework of modeling independencies between attributes
and influence variables. The term “graphical model” is
derived from an analogy between stochastic independence
and node separation in graphs. Let V = {A1 ,... , An } be a set
of random variables. If the underlying probability distribution
P (V ) satisfies some criteria (see e. g. (CGH97; Pea93)),
then it is possible to capture some of the independence
relations between the variables in V using a graph G = (V,
E), where E denotes the set of edges. The underlying idea is
to decompose the joint distribution P (V) into lower-
dimensional marginal or conditional distributions from which
the original distribution can be reconstructed with no or at
least as few errors as possible (LS88; Pea88). The named
independence relations allow for a simplification of these
factor distributions. We claim, that every independence that
can be read from a graph also holds in the corresponding joint
distribution. The graph is then called an independence map
(see e. g. (BSK09)).
Association Rules
The introduction of frequent item set mining and
subsequently association rule induction (AIS93; AMS+ 96)
has created a prospering field of data mining. It is the
simplicity of the underlying concept that allowed for a broad
acceptance among all kinds of users no matter whether they
possess a data analysis background or not. An association rule
is basically an if-then rule. The if -part is called antecedent
while the then -part is named the consequent. Both may
consist of conjunctions of attribute-value pairs, however, the
consequent often consists of only one pair. An example of an
association rule could be
If a person is male and a smoker, his probability of having
lung cancer is 10%.
This corresponds to the imagination that we pick a person
at random from an underlying population (the database) and
observe its properties, which is its attribute values. The above
rule can then be represented in a more formal fashion as
We refer to a database case as being covered by a rule if
the antecedent and consequent attributes values match. For
instance, a smoking man having lung cancer would be
covered by the above rule. The general form of a rule has the
following form:
We will only discuss rules with one consequent attribute
which will be a class variable. We thus use the notions class
and consequent interchangeably.
Since not every database entry matching the antecedent
also matches the consequent it is necessary to record this
information. The probability that a database case matching
the antecedent also matches the consequent, that is P (c | a), is
called the confidence of the rule. The above rule 1 has a
confidence of 0.1. There is a multitude of other measures that
quantify certain aspects of a rule. We will briefly discuss
those that are used in this paper.
The number of cases covered by the rule is referred to as
the (absolute) support of the rule. The relative support equals
P (a, c); it is the absolute support divided by the database
size. The recall quantifies the fraction (or probability if you
keep the above scenario of picking at random) of database
cases matching the antecedent, given the consequent. In other
words: What is the probability of a person being male and a
smoker if this person has cancer? As a last measure (the only
unbounded one) we introduce the lift. It represents the ratio
between the confidence P (c | a) and the marginal consequent
probability P (c): Let the marginal cancer rate be 0.01. Then,
rule 1 has a lift of 10 since the confidence is ten times larger
than the marginal cancer rate. We summarize the measures
below:
- relative support: rel-supp(a → c) = P (a, c)
- confidence: conf(a → c) = P (c | a)
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 9
- recall: recall(a → c) = P (a | c)
- lift: lift(a → c) = P (c | a)
About WEKA tool
There are many tools available for data mining and
machine learning, but in this thesis we use the open source
software suite WEKA which stands for Waikato Environment
for Knowledge Analysis. The main reason why we selected to
use WEKA was because of its versatility. WEKA is a popular
tool used for data analysis, machine learning and predictive
modeling that was developed by the University of Waikato in
New Zealand using the programming language JAVA.
Main Features
Some of WEKAs main features are the following:
Data preprocessing - WEKA supports a couple of popular
text file formats such as CSV, JSON and Matlab ASCII
files to import data along with their own file format
ARFF. They also have support to import data from databases
through JDBC. Besides importing data, they have a wide
collection of supervised as well as unsupervised filters to
apply on your data to facilitate further analysis.
Data classification - A huge collection of algorithms have
been implemented to perform classification on data sets.
These include Bayesian algorithms, mathematical functions
such as support vector machines, lazy classifiers
implementing nearest-neighbor calculations; Meta based
algorithms as well as rule and tree-based classifiers.
Data clustering - A couple of algorithms for clustering
exist such as variations of the k-mean method as well as
density and hierarchical based clustering algorithms.
Attribute association - Methods to analyze data using
association rule learners. Association rules can be seen as
rules describing relations between attributes in a data set.
Attribute selection - Methods to evaluate which attribute
contribute the most when predicting an outcome.
Data visualization - Depending on the methods used to
analyze the data, this view can to plot data against suitable
variables as well as give tools to analyze specific points
further.
III. ID3 ALGORITHM
The ID3 algorithm was originally developed by J. Ross
Quinlan at the University of Sydney, and he first presented it
in the 1975 book “Machine Learning”. The ID3 algorithm
induces classification models, or decision trees, from data. It
is a supervised learning algorithm that is trained by
examples for different classes. After being trained, the
algorithm should be able to predict the class of a new item.
ID3 identifies attributes that differentiate one class from
another. All attributes must be known in advance, and must
also be either continuous or selected from a set of known
values. For instance, temperature (continuous), and country of
citizenship (set of known values) are valid attributes. To
determine which attributes are the most important, ID3 uses
the statistical property of entropy. Entropy measures the
amount of information in an attribute. This is how the
decision tree, which will be used in testing future cases, is
built.
The principle of the ID3 algorithm is as follows. The tree is
constructed top-down in a recursive fashion. At the root,
each attribute is tested to determine how well it alone
classifies the transactions. The “best” attribute (to be
discussed below) is then chosen and the remaining
transactions are partitioned by it.
Entropy
In information theory, entropy is a measure of the
uncertainty about a source of messages. The more uncertain a
receiver is about a source of messages, the more information
that receiver will need in order to know what message has
been sent.
Information gain
Now consider what happens if we partition the set on the
basis of an input attribute X into subsets . The
information needed to identify the class of an element of T is
the weighted average of the information needed to identify
the class of an element of each subset:
In the context of building a decision tree, we are interested
in how much information about the output attribute can be
gained by knowing the value of an input attribute . This is
just the difference between the information needed to classify
an element of before knowing the value of X, H(T), and the
information needed after partitioning the dataset T on the
basis of knowing the value of X, H(X, T). We define the
information gain due to attribute X for set T as:
In order to decide which attribute to split upon, the ID3
algorithm computes the information gain for each attribute,
and selects the one with the highest gain.
The simple ID3 algorithm above can have difficulties when
an input attribute has many possible values, because Gain(X,
T) tends to favor attributes which have a large number of
values. It is easy to understand why if we consider an extreme
case.
Imagine that our dataset contains an attribute that has a
different value for every element of T. This could arise in
practice if a unique record ID was retained when extracting,
from a database.
The problem also arises when an attribute can take on
many values, even if they are not unique to each element.
Quinlan (1986) suggests a solution based on considering the
amount of information required to determine the value of an
attribute X for a set T. This is given by H(PX,T), where PX,T
is the probability distribution of the values of X:
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 10
The quantity H(PX,T) is known as the split information for
attribute X and set T.
ID3 ( Learning Sets S, Attributes Sets A, Attributesvalues V)
Begin
Load training data set for training.
If all examples are positive, return the single-node tree
root with label is positive.
If all examples are negative, return the single-node tree
root with label is negative.
If number predicting attributes is empty, then return the
single node tree root, with the label is most common
value of the target attribute in the examples.
Otherwise
Begin
For rootNode, we compute Entropy(rootNode.subset)
first
If Entropy(rootNode.subset)==0, then
rootNode.subset consists of records all with the
same value for the categorical attribute, return a
leaf node with decision attribute:attribute value;
If Entropy(rootNode.subset)!=0, then compute
information gain for each attribute left(have not
been used in splitting), find attribute A with
Maximum(Gain(S,A)). Create child nodes of this
rootNode and add to rootNode in the decision tree.
For each child of the rootNode, apply ID3(S,A,V)
recursively until reach node that has entropy=0 or
reach leaf node.
End
End
IV. KNN ALGORITHM
The k-nearest neighbor (KNN) algorithm is a simple and
one of the most intuitive machine learning algorithms that
belongs to the category of instance-based learners. Instance-
based learners are also called lazy learner because the actual
generalization process is delayed until classification is
performed, i. e., there is no model building process. Unlike
most other classification algorithms, instance-based learners
do not abstract any information from the training data during
the learning (or training) phase. Learning (training) is
merely a question of encapsulating the training data, the
process of generalization beyond the training data is
postponed until the classification process.
The high degree of local sensitivity makes kNN highly
susceptible to noise in the training data – thus, the value of k
strongly influences the performance of the kNN algorithm.
The optimal choice of k is a problem dependent issue, but
techniques like cross-validation can be used to reveal the
optimal value of k for objects within the training set.
General evaluation considering the simplicity of the KNN
algorithm, the classification results of KNN are generally
quite good and comparable to the performance achieved with
decision trees and rule-based learners. However, the class
specification accuracy of KNN models does in general not
reach the accuracy achieved with support vector machines or
ensemble learners. KNN is considered to be intolerant to
noise, since its similarity measures can easily be distorted by
errors in the attribute values, and is also very sensitive to
irrelevant features. On the contrary, KNN models are
usually not prone to over fitting and can be applied to
incremental learning strategies – since KNN does not build a
classification model, newly classified instances can be added
to the training set easily.
There are several studies that survey the application of
KNN for classification tasks. Besides almost all introductory
data mining books and surveys, that summarizes several
improvements of KNN algorithms for classification.
Distance tables are calculated to produce real-valued
distances from features coming from symbolic domains. It is
standard KNN in three different application domains and has
advantages in training speed and simplicity. On a weight-
adjusted KNN implementation which finds the optimal
weight vector using an optimization function based on the
leave-out-out cross-validation and a greedy hill climbing
technique.
The k-NN search is conducted in two phases. A Z-order-
based approximate proximity mea- sure is used to find the
approximate k-NN. Next, a recursive correction algorithm is
used to improve the accuracy. Another set of techniques is
based on a hybrid of spatial subdivision up to a threshold
granularity and small scale brute force evaluation or
heuristics for refinement. Some techniques take advantage of
the intrinsic dimensionality of the data set to project the data
set into a low dimensional space that preserves proximity.
kNN Algorithm (Set startAndEndPoint, real 𝞮 , int MinC )
Begin
Compute , the distance between z and every object, .
select , the set of k closet training objects to z.
End
V. IMPROVED ID3 WITH TKNN
CLUSTERING ALGORITHM
This Algorithm is characterized by the ability to deal with
the explosion of business data and accelerated market
changes, these characteristics help providing powerful tools
for decision makers, such tools can be used by business users
(not only statisticians) for analyzing huge amount of data for
patterns and trends [11]. Consequently, data mining has
become a research area with increasing importance and it
involved in determining useful patterns from collected data or
determining a model that fits best on the collected data.
It is used to investigate the attributes of car in the
perspective of manufacturer, seller and customer. It is
essential to analyze the car in short span of time, consider
cases when all parties (i.e. manufacturer, seller and customer)
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 11
selecting a right product.
ImprovedID3WithTkNN ( Learning Sets S, Attributes Sets
A, Attributesvalues V, Y )
Begin
1. Load training data set for training.
2. If attributes are uniquely identified in data set, remove it
from training set.
3. On the basis of distance metric divide the given training
data into subsets.
3.1. Calculate the distance for n objects, each
instance in available dataset.
Where X is selected instance and Y is
comparing instance.
4. if D>55% then instance is belong to same group and add
into new set and remove from original data set. Otherwise
do nothing.
5. Repeat the steps 3.1 and 4 for each instance until all
matched it not found.
6. On each subset apply ID3 algorithm recursively.
If all examples are positive, return the single-node
tree root with label is positive.
If all examples are negative, return the single-node
tree root with label is negative.
If number predicting attributes is empty, then return
the single node tree root, with the label is most
common value of the target attribute in the examples.
Otherwise
Begin
For rootNode, we compute
Entropy(rootNode.subset) first
If Entropy(rootNode.subset)==0, then
rootNode.subset consists of records all with the
same value for the categorical attribute, return a
leaf node with decision attribute:attribute value;
If Entropy(rootNode.subset)!=0, then compute
information gain for each attribute left(have not
been used in splitting), find attribute A with
Maximum(Gain(S,A)). Create child nodes of this
rootNode and add to rootNode in the decision
tree.
For each child of the rootNode, apply ID3(S,A,V)
recursively until reach node that has entropy=0 or
reach leaf node.
End
7. Construct TkNN graph among instances.
8. Initialize the similarities on each edge as
and normalize to .
9. Determine the values for all unlabeled data.
10. Compute the label set prediction matrix P.
11. Predict label set for each unbalanced instance by
End
We have executed the same in Weka Tool with Java code
and compared the performance of two algorithms based on
different Percentage Splits to help the car seller/manufacturer
for analyzing their customer views in purchasing a car.
We analyzed the graphical performance analysis between
KNN and our novel improved ID3 with TkNN clustering
algorithms with Classes to Clusters evaluation purchase,
safety, luggage booting, persons (seating capacity), doors,
maintenance and buying attributes of customer’s
requirements for unacceptable/acceptable/good/very good
ratings of a car to purchase.
VI. RESULT ANALYSIS
Improved ID3 Algorithm with TkNN using Percentage
Split 66%
Figure 1: Test Result on Car Data Set with Percentage Split of 66%
using Improved ID3 Algorithm with TkNN
Improved ID3 Algorithm with TkNN Using Training Set
Figure 2: Test Result on Car Data Set with Training Set using
Improved ID3 Algorithm with TkNN
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 12
Improved ID3 Algorithm with TkNN using Classes to Clusters
evaluation safety attribute
Figure 3: Test Result on Car Data Set with Classes to Clusters
evaluation safety attribute
1) Visualize Curve
Improved ID3 Algorithm with TkNN using Training Set
Figure 4: Visualize cluster assignments for Car Data Set using
Improved ID3 Algorithm with TkNN
2) Within cluster sum of squared errors
This error value given cluster is computed by: for each
instance in the cluster, summing the squared differences
between each attributes value and the corresponding one in
the cluster centroid. These are summed up for each instance
in the cluster and for all clusters.
The within cluster sum of squared errors are measured on
all the training data, so selecting the best result that you get is
not necessarily going to be the best for future data due to
possible over fitting.
Within cluster sum of
squared errors
Improved ID3
Algorithm with TkNN
Percentage split 33%
2.319678065
Percentage split 66%
4.574792526
Percentage split 99%
6.871412161
Training Set
6.101260597
Classes to Clusters evaluation
purchase attribute
6.013638562
Classes to Clusters evaluation
safety attribute
5.736937402
Classes to Clusters evaluation
5.257322056
luggage booting attribute
Classes to Clusters evaluation
persons attribute
5.704655599
Classes to Clusters evaluation
doors attribute
5.469459613
Classes to Clusters evaluation
maintenance attribute
5.561693333
Classes to Clusters evaluation
buying attribute
5.575528391
Table 1: Improved ID3 Algorithm with TkNN with Sum of within
cluster distances
Figure 5: Improved ID3 Algorithm with TkNN with Sum of within
cluster distances
3) Number of Iterations
This parameter explains one classifier with training data
and tested against test data with these many specified number
of times.
Number of iterations
Improved
ID3
Algorithm
with TkNN
Percentage split 33%
100
Percentage split 66%
100
Percentage split 99%
100
Training Set
100
Classes to Clusters evaluation
purchase attribute
100
Classes to Clusters evaluation
safety attribute
100
Classes to Clusters evaluation
luggage booting attribute
100
Classes to Clusters evaluation
persons attribute
100
Classes to Clusters evaluation
doors attribute
100
Classes to Clusters evaluation
maintenance attribute
100
Classes to Clusters evaluation
buying attribute
100
Table 2: Improved ID3 Algorithm with TkNN with Number of
Iterations
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 13
Figure 6: Improved ID3 Algorithm with TkNN with Number of
Iterations
4) Cluster Instances of TkNN Algorithm
attribute
Total(1728)
Clster0(1062)
Cluster1(666)
buying
vhigh
low
vhigh
maint
vhigh
vhigh
high
doors
2
5more
2
persons
2
more
2
lug_boot
small
small
small
safety
low
low
hig
purchase
unacc
unacc
unacc
Table 3: Buying attribute Comparison on TkNN and
ImprovedID3withTkNN
5) Cluster Instances of Improved ID3 Algorithm with
TkNN Algorithms
attribute
Total(1728)
Clster0(1524)
Cluster1(204)
buying
vhigh
vhigh
high
maint
vhigh
vhigh
med
doors
2
2
4
persons
2
2
4
lug_boot
small
small
big
safety
low
low
high
purchase
unacc
unacc
acc
Table 4: Buying attribute Comparison on TkNN and
ImprovedID3withTkNN
A. Buying attribute Comparison on TkNN and
ImprovedID3withTkNN
TkNN
(cluster
0)
Improved
ID3 with
TkNN
(cluster 0)
TkNN
(cluster
1)
Improved
ID3 with
TkNN
(cluster 1)
Very
High
1524
666
High
204
Medi
um
204
Low
1062
Table 5: Buying attribute Comparison on TkNN and
ImprovedID3withTkNN
Figure 7: Buying attribute Comparison on TkNN and
ImprovedID3withTkNN
B. Maintenance attribute Comparison on TkNN and
ImprovedID3withTkNN
TkNN
(cluste
r 0)
Improved ID3
with TkNN
(cluster 0)
TkNN
(cluster
1)
Improved
ID3 with
TkNN
(cluster 1)
Very
High
1062
1524
High
666
Medi
um
204
Low
Table 6: Maintenance attribute Comparison on TkNN and
ImprovedID3withTkNN
Figure 8: Maintenance attribute Comparison on TkNN and
ImprovedID3withTkNN
C. Doors attribute Comparison on TkNN and
ImprovedID3withTkNN
TkNN
(cluster
0)
Improved
ID3 with
TkNN
(cluster 0)
TkNN
(cluster
1)
Improved
ID3 with
TkNN
(cluster 1)
5 or
More
Doors
1062
4
Doors
204
3
Doors
2
Doors
1524
666
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 14
Table 7: Doors attribute Comparison on TkNN and
ImprovedID3withTkNN
Figure 9: Doors attribute Comparison on TkNN and
ImprovedID3withTkNN
D. Persons attribute Comparison on TkNN and
ImprovedID3withTkNN
TkNN
(cluster
0)
Improved
ID3 with
TkNN
(cluster
0)
TkNN
(cluster
1)
Improved
ID3 with
TkNN
(cluster
1)
More
Persons
1062
204
4
Persons
2
Persons
1524
666
Table 8: Persons attribute Comparison on TkNN and
ImprovedID3withTkNN
Figure 10: Pesons attribute Comparison on TkNN and
ImprovedID3withTkNN
E. Luggage Booting attribute Comparison on TkNN and
ImprovedID3withTkNN
TkNN
(cluster
0)
Improved
ID3 with
TkNN
(cluster 0)
TkNN
(cluster
1)
Improved
ID3 with
TkNN
(cluster 1)
Big
204
Medium
Small
1062
1524
666
very high
Table 9: Luggage Booting attribute Comparison on TkNN and
ImprovedID3withTkNN
Figure 11: Luggage Booting attribute Comparison on TkNN and
ImprovedID3withTkNN
F. Safety attribute Comparison on TkNN and
ImprovedID3withTkNN
TkNN
(cluster
0)
Improved
ID3 with
TkNN
(cluster 0)
TkNN
(cluster
1)
Improved
ID3 with
TkNN
(cluster 1)
High
666
204
Medium
Low
1062
1524
Table 10: Safety attribute Comparison on TkNN and
ImprovedID3withTkNN
International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, Jul-Aug 2014
ISSN: 2347-8578 www.ijcstjournal.org Page 15
Figure 12: Safety attribute Comparison on TkNN and
ImprovedID3withTkNN
G. Purchase attribute Comparison on TkNN and
ImprovedID3withTkNN
TkNN
(cluster
0)
Improved
ID3 with
TkNN
(cluster 0)
TkNN
(cluster
1)
Improved
ID3 with
TkNN
(cluster 1)
Very Good
Good
Acceptable
204
Un
Accaptable
1062
1524
666
Table 11: Purchase attribute Comparison on TkNN and
ImprovedID3withTkNN
Figure 13: Purchase attribute Comparison on TkNN and
ImprovedID3withTkNN
VII. CONCLUSION
This Paper presents ID3, KNN, TkNN and our novel
improved ID3 with TkNN clustering algorithms. We have
also executed the same in Weka Tool with Java code and
compared the performance of two algorithms based on
different Percentage Splits to help the car seller/manufacturer
for analyzing their customer views in purchasing a car.
We analyzed the graphical performance analysis between
KNN and our novel improved ID3 with TkNN clustering
algorithms with Classes to Clusters evaluation purchase,
safety, luggage booting, persons (seating capacity), doors,
maintenance and buying attributes of customer’s
requirements for unacceptable/acceptable/good/very good
ratings of a car to purchase.
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BIOGRAPHY
M.Jayakameswaraiah,Received his Master of Computer
Applications Degree from Sri Venkateswara
University,Tirupati, Andhrapradesh, INDIA in 2009 and
Currently Pursuing Ph.D in Computer Science from Sri
Venkateswara University ,Tirupati, Andhrapradesh,
INDIA.The Research fields of interest are Data Mining, Cloud
Computing, Software Engineering and Data Base
Management System etc. E-Mail:
mjayakameswaraiah@gmail.com
Prof.S.Ramakrishna, Professor in Department of
Computer Science, Sri Venkateswara University,Tirupati,
Andhrapradesh,India.His Areas of interest include Data
Mining, Software Engineering,Data Base Management
System, Image Processing and Machine learning etc. E-Mail:
drsramakrishna@yahoo.com
