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Intelligent Data Analysis (IDA) and Visualization

Dr. Nancy Agens, Head,

Technical Operations, Phdassistance

info@phdassistance.com

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,

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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

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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.