Intelligent Data Analysis (IDA) And Visualization - Phdassitance.com
-
Upload
lopezphdassistance -
Category
Education
-
view
0 -
download
0
description
Transcript of Intelligent Data Analysis (IDA) And Visualization - Phdassitance.com
Copyright © 2020 PhdAssistance. All rights reserved 1
Intelligent Data Analysis (IDA) and Visualization
Dr. Nancy Agens, Head,
Technical Operations, Phdassistance
In Brief
You will find the best dissertation research
areas / topics for future researchers
enrolled in Computer Science &
Information. In order to identify the future
research topics, we have reviewed the
computer science (recent peer-reviewed
studies) on Data Analysis. Process of
finding and identifying the meaning of
data. Main advantage of visual
representations is to discover, make sense
of data and communicating data.
Keywords: Artificial Intelligence, Data
analysis, Data Visualization, Data Mining.
I. DATA
Data is nothing but things known or
anything that is assumed; facts from which
conclusions can be gathered.
II. DATA ANALYSIS
Breaking up of any data into parts i.e.,
the examination of these parts to know
about their nature, proportion, function,
interrelationship, etc.
A process in which the analyst moves
laterally and recursively between three
modes: describing data (profiling,
correlation, summarizing), assembling
data (scrubbing, translating,
synthesizing, filtering) and creating data
(deriving, formulating, simulating).
It is a sense of making data. The process
of finding and identifying the meaning
of data.
III. DATA VISUALIZATION
It is a process of revealing already
existing data and/or its features (origin,
metadata, allocation), which includes
everything from the table to charts and
multidimensional animation (Min Yao,
2014) .
To form an intellectual image of
something not there to the sight.
Visual data analysis is another form of
data analysis, in which some or all forms
of data visualization may be used to give
feedback sign to the analyst. Our product
uses visual signs such as charts,
interactive browsing, and workflow
process cues to help the analyst in
moving through the modes of data
analysis.
The main advantage of visual
representations is to discover, make
sense of data and communicating data.
Data visualization is a central part and an
essential means to carry out data analysis
and then, once the importance have been
identified and understood, it is easy to
communicate those meanings to others.
IV. IMPORTANCE OF IDA
Intelligent Data Analysis (IDA) is
one of the major issues in artificial
intelligence and information. Intelligent data
analysis discloses hidden facts that are not
known previously and provides potentially
important information or facts from large
quantities of data (White, 2008). It also
helps in making a decision. Based on
machine learning, artificial intelligence,
Copyright © 2020 PhdAssistance. All rights reserved 2
recognition of pattern, and records and
visualization technology mainly, IDA helps
to obtain useful information, necessary data
and interesting models from a lot of data
available online in order to make the right
choices.
Intelligent data analysis helps to
solve a problem that is already solved as a
matter of routine. If the data is collected for
the past cases together with the result that
was finally achieved, such data can be used
to revise and optimize the presently used
strategy to arrive at a conclusion.
In certain cases, if some questions
arise for the first time, and have only a little
knowledge about it, data from the related
situations helps us to solve the new problem
or any unknown relationships can be
discovered from the data to gain knowledge
in an unfamiliar area.
V. STEPS INVOLVED IN IDA
IDA, in general, includes three
stages: (1) Preparation of data; (2) data
mining; (3) data validation and explanation
(Keim & Ward, 2007). The preparation of
data involves opting for the required data
from the related data source and
incorporating it into a data set that can be
used for data mining.
The main goal of intelligent data
analysis is to obtain knowledge. Data
analysis is the process of a combination of
extracting data from data set, analyzing,
classification of data, organizing, reasoning,
and so on. It is challenging to choose
suitable methods to resolve the complexity
of the process.
Regarding the term visualization, we
have moved away from visualization to use
the term charting. The term analysis is
used for the method of incorporating,
influencing, filtering and scrubbing the data,
which certainly contains, but is not limited
to interrelating with their data through
charts.
VI. THE GOAL OF DATA ANALYSIS
Data analysis need not essentially
involve arithmetic or statistics. While it is
true that analysis often involves one or both,
and that many analytical pursuits cannot be
handled without them, much of the data
analysis that people perform in the course of
their work involves at most mathematics no
more complicated than the calculation of the
mean of a set of values. The essential
activity of analysis is a comparison (of
values, patterns, etc.), which can often be
done by simply using our eyes.
The aim of the analysis is not to find
out appealing information in the data.
Rather, this is only a vital part of the process
(Berthold & Hand, 2003). The aim is to
make sense of data (i.e., to understand what
it means) and then to make decisions based
on the understanding that is achieved.
Information in and of itself is not useful.
Even understanding information in and of it
is not useful. The aim of data analysis is to
make better decisions.
The process of data analysis starts
with the collection of data that can add to the
solution of any given problem, and with the
organization of that data in some regular
form. It involves identifying and applying a
statistical or deterministic schema or model
of the data that can be manipulated for
explanatory or predictive purposes. It then
involves an interactive or automated solution
that explores the structured data in order to
extract information – a solution to the
business problem – from the data.
VII. THE GOAL OF VISUALIZATION
The basic idea of visual data mining
is to present the data in some visual form,
allowing the user to gain insight into the
Copyright © 2020 PhdAssistance. All rights reserved 3
data, draw conclusions, and directly interact
with the data. Visual data analysis
techniques have proven to be of high value
in exploratory data analysis. Visual data
mining is mainly helpful when the only little
fact is known about the data and the
exploration goals are indistinct.
VIII. THE MAIN USES OF VISUAL
DATA EXAMINATION OVER DATA
ANALYSIS METHODS ARE
Visual data examination can simply deal
with highly non-homogeneous and noisy
data.
Visual data exploration is spontaneous
and requires no knowledge of complex
mathematical or arithmetical algorithms
or parameters.
Visualization can present a qualitative
outline of the data, letting data
phenomenon to be secluded for further
quantitative analysis. Accordingly,
visual data examination usually allows a
quicker data investigation and often
provides fascinating results, especially in
cases where automatic algorithms fail.
Visual data examination techniques
provide a much higher degree of
assurance in the findings of the
exploration.
IX. CONCLUSION
The examination of large data sets is
a significant but complicated problem.
Information visualization techniques can be
helpful in solving this problem. Visual data
investigation is helpful for many purposes
such as fraud detection system and data
mining can make use of data visualization
technology for improved data analysis.
REFERENCES
[1] Berthold, M. & Hand, D.J. (2003). Intelligent data
analysis. [Online]. Springer. Available from:
https://link.springer.com/content/pdf/bfm%253A978
-3-540-48625-1%252F1.pdf.
[2] Keim, D. & Ward, M. (2007). Visualization. In:
Intelligent Data Analysis. [Online]. Berlin,
Heidelberg: Springer Berlin Heidelberg, pp. 403–
427. Available from:
http://link.springer.com/10.1007/978-3-540-48625-
1_11.
[3] Min Yao (2014). Special Issue ‘Intelligent Data
Analysis’. [Online]. Available from:
https://www.mdpi.com/journal/information/special_i
ssues/data-analysis?view=abstract&listby=type.
[4] White, C. (2008). Business Intelligence Data Analysis
and Visualization: What’s in a Name? Part 1.
[Online]. Available from: http://www.b-eye-
network.com/view/9336.