Page 1 of 8
Journal for Studies in Management and Planning
Available at http://edupediapublications.org/journals/index.php/JSMaP/
ISSN: 2395-0463
Volume 03 Issue 01
January 2017
Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 105
Knowledge Discovery Conduted in the Areas of Machine
Learning By High Performance Computing Using Evalution of
Learning Algorithms
V.Rama Rao
Associate Professor, Gandhi Academy of Technical Education, Kodad, Telangana.
ABSTRACT:
Knowledge discovery in databases (KDD)
The latest research in the field of statistics,
machine learning, Amnesty International.
This is part of the area of the rapid growth
of data Mining and knowledge discovery.
The topics covered here Major issues,
sorting, assembling and Application. The
various stages of data collection and
research Questions focused. The nature of
clinical data makes it difficult to quickly
select, tune and apply machine learning
algorithms to clinical prognosis. As a result,
a lot of time is spent searching for the most
appropriate machine learning algorithms
applicable in clinical prognosis that
contains either binary-valued or multi- valued attributes. The study set out to
identify and evaluate the performance of
machine learning classification schemes
applied in clinical prognosis of post- operative life expectancy in the lung cancer
patients. Multilayer Perceptron, J48, and
the Naive Bayes algorithms were used to
train and test models on Thoracic Surgery
datasets obtained from the University of
California Irvine machine learning
repository. Stratified 10-fold cross- validation was used to evaluate baseline
performance accuracy of the classifiers. The
comparative analysis shows that multilayer
perceptron performed best with
classification accuracy of 82.3%, J48 came
out second with classification accuracy of
81.8%, and Naive Bayes came out the worst
with classification accuracy of 74.4%. The
quality and outcome of the chosen machine
learning algorithms depends on the
ingenuity of the clinical miner.
I. INTRODUCTION
Data mining is considered to be an emerging
technology that has made revolutionary
change in the information world. The term
`data mining' (often called as knowledge
discovery) refers to the process of analyzing
data from different perspectives and
summarizing it into useful information by
Page 2 of 8
Journal for Studies in Management and Planning
Available at http://edupediapublications.org/journals/index.php/JSMaP/
ISSN: 2395-0463
Volume 03 Issue 01
January 2017
Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 106
means of a number of analytical tools and
techniques, which in turn may be useful to
increase the performance of a system.
Technically, “data mining is the process of
finding correlations or patterns among
dozens of fields in large relational
databases”. Therefore, data mining consists
of major functional elements that transform
data onto data warehouse, manage data in a
multidimensional database, facilitates data
access to information professionals or
analysts, analyze data using application tools
and techniques, and meaningfully presents
data to provide useful information.
According to the Gartner Group, ``data
mining is the process of discovering
meaningful new correlation patterns and
trends by sifting through large amount of
data stored in repositories, using pattern
recognition technologies as well as
statistical and mathematical techniques''[3] .
Thus use of data mining technique has to be
domain specific and depends on the area of
application that requires a relevant as well as
high quality data. More precisely, data
mining refers to the process of analyzing
data in order to determine patterns and their
relationships[9]. It automates and simplifies
the overall statistical process, from data
source(s) to model application. Practically
analytical techniques used in data mining
include statistical methods and mathematical
modeling. However, data mining and
knowledge discovery is a rapidly growing
area of research and application that builds
on techniques and theories from many
fields, including statistics, databases, pattern
recognition, data visualization, data
warehousing and OLAP, optimization, and
high performance computing [1] . Worthy to
mention that online analytical processing
(OLAP) is quite different from data mining,
though it provides a very good view of what
is happening but cannot predict what will
happen in the future or why it is happening.
In fact, blind applications of algorithms are
not also data mining. In particular, ``data
mining is a user centric interactive process
that leverages analysis technologies and
computing power, or a group of techniques
that find relationships that have not
previously been discovered'' [4] . So, data
mining can be considered as a convergence
of three technologies -- viz. increased
computing power, improved data collection
and management tools, and enhanced
statistical algorithms. Data and information
have become major assets for most of the
organizations. The success of any
organization depends largely on the extent to
Page 3 of 8
Journal for Studies in Management and Planning
Available at http://edupediapublications.org/journals/index.php/JSMaP/
ISSN: 2395-0463
Volume 03 Issue 01
January 2017
Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 107
which the data acquired from business
operations is utilized. In other words, the
data serves as an input into a strategic
decision making process, which could put
the business ahead of its competitors[10,11].
Also, in this era, where businesses are
driven by the customers, having a customer
database would enable management in any
organization to determine customer behavior
and preference in order to offer better
services and to prevent losing them resulting
better business. The data needed that will
serve as an input to organizational decision- making process is generated and
warehoused. It is being collected via many
sources, such as the point of sales
transactions, surveys, through the internet
logs – cookies, etc. This has resulted in huge
databases which have valuable knowledge
hidden in them and may be difficult to
extract. Data mining has been identified as
the technology that offers the possibilities of
discovering the hidden knowledge from
these accumulated databases. Techniques
such as pattern recognition and classification
are the most important in data mining [4,5].
The task of recognition and classification is
one of the most frequently encountered
decision making problems in daily activities.
A classification problem occurs when an
object needs to be assigned into a predefined
group or class based on a number of
observed attributes, or features, related to
that object. Humans constantly receive
information in the form of patterns of
interrelated facts, and have to make
decisions based on them. When confronted
with a pattern recognition problem, stored
knowledge and past experience can be used
to assist in making the correct decision.
Indeed, many problems in various domains
such as financial, industrial, technological,
and medical sectors, can be cast as
classification problems. Examples include
bankruptcy prediction, credit scoring,
machine fault detection, medical diagnosis,
quality control, handwritten character
recognition, speech recognition etc. Pattern
recognition and classification has been
studied extensively in the literature. In
general, the problem of pattern recognition
can be posed as a two-stage process:
Feature selection which involves
selecting the significant features from an
input pattern
Classification which involves devising a
procedure for discriminating the
measurements taken from the selected
features, and assigning the input pattern into
