Intelligent Data Analysis (IDA) and Visualization - Phdassistance.com

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INTELLIGENT DATA ANALYSIS (IDA) AND
VISUALIZATION






An Academic presentation by Dr. Nancy Agens,
Head, Technical Operations, Phdassistance
Group www.phdassistance.com Email
info_at_phdassistance.com
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TODAY'S DISCUSSION
Outline
In Brief Data Data Analysis Data Visualization
Importance of IDA Steps Involved In IDA
Data Analysis Goals Visualization Goals Uses of
Visual Data Examination over Data Analysis
Methods
Conclusion
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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.

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Data
Data is nothing but things known or anything that
is assumed facts from which conclusions can be
gathered.
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Breaking up of any data into parts i.e., the
examination of these parts to know about their
nature, proportion, function, interrelationship,
etc.
Data Analysis

• 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.
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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. 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.
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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.
Importance of IDA
Based on machine learning, artificial
intelligence, recognition of pattern, and
records and visualization technology mainly, IDA
helps to obtain useful information, necessary
data and interesting models.
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IDA, in general, includes three stages

1. Preparation of data
2. data mining
3. data validation and explanation.

Steps Involved In IDA
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. The term analysis is used for the
method of incorporating, influencing, filtering
and scrubbing the data.
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Data Analysis Goals
Data analysis need not essentially involve
arithmetic or statistics. The essential activity
of analysis is a comparison.. 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.
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Visualization Goals
The basic idea of visual data mining is to
present the data in some visual form, allowing
the user to gain insight into the 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.
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Uses of Visual Data Examination over Data
Analysis Methods
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. Visualizat
ion can present a qualitative outline of the
data, letting data phenomenon to be secluded for
further quantitative analysis. Visual data
examination techniques provide a much higher
degree of assurance in the findings of the
exploration.
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The examination of large data sets is a
significant but complicated problem.
Conclusion
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.
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