Data Mining – Arun K. Pujari

303 Pages·2010·3.49 MB·English

DATA
MINING

TECHNIQUES

eet
yor"

Arun K Pujari

seonrte seo a
ETE WITHIN

wy
Universities Press
smadAnt Aes FILLER. Whoa

Universes Press nia) Private

Reginve ier
BETEEUA 58 1841 Timayatonge
pera 500029 (8.75. lie
lin ance cron

2 Univerennas Poss indian Patel nated 200

Fis sasched2081
es ines 2008

ISBS-L5 97H RI TS71 ARC
SSeS.) RUPE Od

pared
Hysbrshea 1039
Pete tot se
Graphs Pritts
Hyleasad $00 013

Pati i
aiversitie Pe edit Pvt Limited
SE MMUA 26.7041 Himayamaeae
pra SHIIES CA, Ea
w
ay ronthor

Late Sd. Tatas Des

= aver:

fragile and weak tad se ws the pillar of strength Jor all her children
‘nd er husband fa team on
ConTENTS AT A GLANCE

Iwrropuerion
‘Data WAREHOUSING

Dara Minin:

Associrion RULES

Cuustenine TecHmoves

Decision Tree TecHuraues

‘OtHen TecumauEs

Wee Minis

‘Temponat ano Srarian Data MINING
List oF Contents

Fomor “i
Protoatx

Prairace
ACKRENLEDCEMENT

1 tvexopucras '
LA Iseoduetion 1
12 ata Ming as & Subject, 4
13 Gide tothis Book 5

2 Dara Wanenowsse 7
21 rwraduetion 7

22 What isa Data Warehuuse? °

23 Definition lo

2.4 Multidimensiousl Date Medet W

2S OLAP Operations u

26 Warehouse Schema “0
2.7 Dota Warehousing Arstitootore cy
2B Warchnuse Server a
29 Metwiata %
2.10 OLAP Engine 2B
BLL Data Warchouse Backend Process cf
2.12 Other Feacuss 31
213 Summary x

Exercioos 3
Bibliography 0

3 Dara Moms 2
3 lateedvetion 2
32 Whalis Date Mining? B
3.3 Data Mining: Detiutions 4
34 KDD vs. Paw Mining a
34 DBMS wsDM ”
36 Other Relued Areas st
32 DM Techniques 31

341 Other Mining Prablems 3
wii Des Mining Tenis

$9 Teaver and Caallanges in BM. 8
BAN DM Apploation Areas “0
BAL DM Applicetions—Cuve Sues a
$12 Conclusion 6

Furs Realing KG
Exercises o
Bibliowsehy co

4 Awpuauias Russ oo
4. Introduction °
42. Whatsgan Association Rule? i
4.3. Mottedste Dicenvar Assoo-ation Rules B
44° APeiur Algocithay
45 Partition Algorithm
4.6 Fincer-Ssare: Algorithm BS
4.7 Dytuie lentset Coursing Algorithm 0
44 Petree Cromth Alport, on
49. Digeus.on or iffeenl Algoritims 8
440. feorementl Auris 10
SLL. Roger Algerithn 102
4.12 Genecilized Association Rule ns

43. Assocation Rules With Hem Cone
414: Summary
Facet Reading
Cacrcises
Riliography
§ Ciustenye Teen wes

Tnlroduction
Chasen Paraligats

Paririoning Slgorithns

keMedoid Aigorshms

CLARA

CLARANS,

Teeearhica: Clustoring

DESCAN

BIRCH

CURE,

Categorical Case AlgorStuns
SUIRK

ROCK

CACTES,
515 Conchuon
Fuchor Reading
Exotics
Bitliography

Dosen Deets

6.1 Traraducsion

6.2 What isa Dovsion Te

63 Tree Construction Principle

fia Best Spht

65. Splitang indices

66 Splicing Crvera

62 Devision Tioe Constracton Aluorithrs

fd CART

6.13. Devssion Troe Construction vita resorting
6.14 Rainforest
6.13 Approeimate Methods
6.16 CLOUDS
617 BOAT
6.18 Proning Technique
6.19 ategeatin of poning and consiroction
‘Summary: An Edel Algosivhm
Otier Topics
622 Conclusion
other Reulimg
Hrcreies
Bibliography

Onuen Tecngens
TL Introduction

72. What is a Nowal Netwusk?

72 Learning ia NM

74 Unvarervisel Leaning

15. Data Mining using NN: A Case Seedy
7.6 Genetic gorithm

73 Rouph Sets

ER Suppotl Veotur Machines

79° Conclusion

on

1a
ra
1a
1

19
Ge Mining Tedsigues

ure Reading

Excrcies
Tibliegraphy

8 Wee Minin
SL Inledust:

2) WebMining
¥3- Web Content Mining
A WebStrucore Mii,
sage Mir

ke Ten Mine

Cesmuctatse Tex
83. Episorte Rule Duscovery fur Texts
K9-Hieraschy vf Catogories
8.19 TerClusering
14 Corclusive

Furker Resding

Bitioazaphy

Addendum

9 Tresrogas asp Seanad Daa MINING

9. taleedoction
92 Weat is Texapoeal Bala Mining?
9.3 Temporal Assuciatan Raise

9A Sequence Minty

93 The GSP Algorithes

6 SPADE
97 SPT
93 WUM

9.9 Epil Discovery
9.10 sent Predieson Pendle
941 Time sores Analysis
AE Spall Mince
3.13 Spanal Mimsy Tasks
944 Spatial Chute,
95. Spatia! Freud
3.16 Cypelusion
urtaer Reading
Exerises
Bitliowsephy

INU
FOREWORD

‘Vasc amouns of peratvnal Ua ae routinely coMlected and sore ave inte archives of
many orguncanins. Ta cake 8 sinple example, the ralway reservation syn haz beet
vperiona! for over a deca and a vast srivuat ul Usa f generated each day on tai
bookings. Much of this data is probably archived for audi pugpoven. "hi archived
coperatianaldsta can be efecively used fer tached! stratsie mitayennent ofthe raya.
ue insince, hy analyzing the -eservaion data wt auld he possible fd oul wae
paler wr various sectors and wae ith add or iemowe bute i crtin tas, to dtide om
the mis of varivus elases uf accommodation, ete, This, Inwever, was not Feasible und
‘oeenmy duc w limitations in beth hardware and software. Tote lt Ave yeas there ba
boon a trontendous improversea i hardware 5:2 MBE af main memory, 40 GB ask and
CPU clock vf £ 6 GAY as now avalb'e an a PC Thus, cemmputerprngeans which sil
massive arscums uf opecaional dul, eecogniza patterns and previde ints 12 fornia
Irytheses for acti aud static Ueision-making can naw be executed 2 easerable
‘me. This has aponed op file ate of rewazch ro frmulae ayn algocitms Ro
mining archival data i forrulce and Wal hypotkece. Aa uray of Has Fm computer
stience. sates and manageron scones ae beg pied in Us are, This Suh: is
cme active research aa,

1 ars extemely tuppy tbat Profeswor Aran K, Puja bat writen this book on Mack
‘Mining Techaigues Il ley adition wo the exiting comple sence iterate and
‘will be a boon to studeals Mrofeeor Pyar. has been an aclveratearchey i thi area, He
Jas als intrdooed a coorss on Dats Ming a tke University of Hydcabad and ns bora
feaching ic His rice expeconoe in teaching amt research is evident in the aeleeion of
topics and tele eatment inthis hook. The book sis wilh brie intoduction followed
bya dctafed chap on dala naccheusing,Ithas chapters whisk disease a Tange number of
sivereealgorigzms using saucalion mle, clustering sechniqus, dsicion tre techniques,
rneuual newark, genetics, rough sel, ad supper weer machines. A cel aspect ofthis
nok iss diseussion ofthe enecying are of wel ming of extual dat, A Tage Nucuber
freferences are cit and the mestmet slid and pw date, Irecazimend this Pook 2
sludens wha want io explre tis ipeisul sect

V-Rajaramen,
Manarry Professor
Super Compuler Edueaiion & Research Cer
Tadign Ise of Scien, Bangalore