Intelligent Data Analysis (IDA) and Visualization - Phdassistance.com
                     INTELLIGENT DATA  
ANALYSIS (IDA) AND  
VISUALIZATION
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
TODAY'S 
DISCUSSION
Outlin
e
In Brief Data Analysis  Goals
Data Visual izat ion  Goals
Data Analysis  Uses of  Visual  Data Examinat ion  over  Data 
Data Visual izat ion  Analysis  Me thods
 Impor tance  of  Con c lu sion
IDA
Steps Invo lved  In 
IDA
In 
You will find the best disseBrtartioine refsearch 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.
Dat
a
Data is nothing but things known or anything that is assumed; 
 facts from which conclusions can be gathered.
Breaking up of any data into parts i.e., the examination of these parts 
 to know about their nature, proportion, function, interrelationship, 
etc.
Data  A process in which the analyst moves laterally and recursively 
 between three modes:
Analysi 1. Describing data (profiling, correlation, summarizing),
2. Assembling data (scrubbing, translating, synthesizing, 
s filtering) and
3. Creating data (deriving, formulating, simulating).
It is a sense of making data. The process of finding and identifying the 
 meaning of data.
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.
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 
Importanc  previously and provides potentially important information or facts 
e  of IDA:  from large quantities of data.
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.
IDA, in general, includes three stages:
1. Preparation of data;
2. data mining;
3. data validation and explanation.
Steps  
Involved The main goal of intelligent data analysis is to obtain knowledge.
In  IDA:
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.
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.
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.
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.
Visualization 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.
The examination of large data sets is a significant but  
complicated problem.
Conclusio Information visualization techniques can be helpful in 
n  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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