Intelligent Data Analysis: Understanding IDA, Tools, and Techniques, Slides of Public Health

Download Intelligent Data Analysis: Understanding IDA, Tools, and Techniques and more Slides Public Health in PDF only on Docsity!

Intelligent Data

Analysis

(IDA)

Interest and Excitement forIntelligent Data Analysis^ 

Decision making is asking forinformation and knowledge Data processing can give them Multidimensionality of problems islooking for methods for adequate anddeep data processing and analysis

Performance Objectives

Recognize problems asking for IDA Preparing data and making analysis Validating and interpreting results of IDA

IDA is…

… an interdisciplinary studyconcerned with the effectiveanalysis of data;… used for extracting usefulinformation from large quantitiesof online data; extracting desirableknowledge or interesting patternsfrom existing databases;

IDA gives knowledge …

Knowledge is …

^ the distillation of information that has beencollected, classified, organized, integrated,abstracted and value-added; ^ at a level of abstraction higher than the data,and information on which it is based and canbe used to deduce new information and newknowledge; ^ usually in the context of human expertiseused in solving problems.

Knowledge in a domain canbe expressed as a number

of rules

Rule is …

A formal way of specifying arecommendation, directive, orstrategy, expressed as "IF premiseTHEN conclusion" or "IF conditionTHEN action".

Some tools for IDA …

 See

• program for analyzing data and generating classifiers in the form ofdecision trees and/or rule sets.

Some tools for IDA …

 Cubist

• analyzes data and generates rule-based piecewise linear models –collections of rules, each with anassociated linear expression forcomputing a target value..

Some tools for IDA …

 Magnum Opus

- finds association

rules providing competitiveadvantage by revealing underlyinginteractions between factors withinthe data.

Evaluation of IDA results Absolute & relative accuracy Sensitivity & specificity False positive & false negative Error rate Reliability of rules Etc.

See5…application…

application.

names

• lists the

classes

to

which cases may belong and the attributes

used to describe each case.

Attributes are of two types:

discrete

attributes have a value drawn from a setof possibilities, and

continuous

attributes have numeric values.

See5…application…

application.

data

• provides information

on the

training

cases from which See

will extract patterns. The entry for each case consists of oneor more lines that give the values for allattributes.