lecture-01 (data mining concepts & technologies)

8/8/2019 Lecture-01 (Data Mining Concepts & Technologies)

1/13

Lecture_01


2/13

Data mining has attracted a greatdeal of

attention in the industry and in the

society as a whole in recentyears,duetothe wide availability of huge amounts of

data andthe need forturning such data

into useful information and knowledge.


3/13

The fast growing, tremendous amount of

data, collected and stored in large and

numerous data repositories, has farexceeded our human ability for

comprehension.This situation has been

described as data rich but information

poor situation.These largedatarepositories become data tombs----

data archives that are seldom visited.


4/13

Consequently, importantdecisions are

often made based not on the information

rich data stored in data repositories, butrather on a decision makers intuition.

Simply becausethedecision makerdoes

not havethetools to extractthe valuable

knowledgeembedded in the vast amountofdata.


5/13

Data mining refers to extracting or

mining knowledge from large amounts

ofdata.Actuallydata mining tools perform data

analysis and mayuncover importantdata

patterns, contributing greatlyto business

strategies, knowledge bases andscientific and medical research.


6/13

Data mining refers to theprocess of findinginteresting information in largerepositories ofdata

Theterm data mining also refers to the step inthe knowledgediscoveryprocess in whichspecial algorithms areused in hopes ofidentifying interesting patterns in data.Theseinteresting patterns arethen analyzedyielding

knowledge.Thedesired outcome ofdata miningactivities is to discover knowledgethat is notexplicit in thedata, andto putthat knowledgetouse


7/13

1960s Data Collection anddatabasecreation

1970s Relational data Model

1980s Relational Database Management System

1990-2000s Data Warehousing and Data Mining


8/13

Theterm data mining is a misnomer.

Alternative Names

Knowledgediscovery(mining) in

databases (KDD), knowledgeextraction,

data/pattern analysis,data archeology,

data dredging, information harvesting,business intelligence, etc.


9/13

Data Cleaning

Data WarehouseSelection

Task-relevant Data

Data Mining

Data Integration

Pattern Evaluation

Databases


10/13

Data Cleaning:To remove noise and

inconsistentdata.

Data Integration:Where multipledata

sources may becombined.Data cleaning

anddata integration areperformed as a

preprocessing step, wheretheresultingdata is stored in a data warehouse.


11/13

Data Selection:Wheredata relevantto the

analysis task areretrieved from the

database.

Data Transformation:Wheredata is

transformed orconsolidated into forms

appropriate for mining.


12/13

Data Mining:Where intelligent methods areapplied in orderto extractdata patterns.

Pattern evaluation:T

o identifythetrulyinteresting patterns representingknowledge, based on some interestingnessmeasures.

Knowledgerepresentation: wherevisualization and knowledgerepresentationtechniques areusedto presentthe minedknowledge to theuser.


13/13

lecture-01 (data mining concepts & technologies)

Documents