Fundamentals, Design, and Implementation, 9/e Chapter 10 Managing Databases with Oracle 9i SII 654 Fall 2005.

Fundamentals, Design, and Implementation, 9/e Chapter 10 Managing Databases with Oracle 9i SII 654 Fall 2005

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/2 Copyright © 2004 Introduction  Oracle is the world’s most popular DBMS  It is a powerful and robust DBMS that runs on many different operating systems  Oracle DBMS engine: Personal Oracle and Enterprise Oracle  Example of Oracle products –SQL*Plus: a utility for processing SQL and creating components like stored procedures and triggers PL/SQL is a programming language that adds programming constructs to the SQL language –Oracle Developer (Forms & Reports Builder) –Oracle Designer

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/3 Copyright © 2004 Creating an Oracle Database  Installing Oracle –Install Oracle 9i Client to use an already created database –Install Oracle 9i Personal Edition to create your own databases  Three ways to create an Oracle database –Via the Oracle Database Configuration Assistant –Via the Oracle-supplied database creation procedures –Via the SQL CREATE DATABASE command

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/4 Copyright © 2004 SQL*Plus  Oracle SQL*Plus or the Oracle Enterprise Manager Console may be used to manage an Oracle database  SQL*Plus is a text editor available in all Oracle  Except inside quotation marks of strings, Oracle commands are case-insensitive  The semicolon (;) terminates a SQL statement  The right-leaning slash (/) executes SQL statement stored in Oracle buffer  SQL*Plus can be used to –Enter SQL statements –Submit SQL files created by text editors, e.g., notepad, to Oracle

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/6 Copyright © 2004 SQL*Plus Buffer  SQL*Plus keeps the current statements in a multi-line buffer without executing it  LIST is used to see the contents of the buffer –LIST [line_number] is used to change the current line  CHANGE/astring/bstring/ is used to change the contents of the current line –astring = the string you want to change –bstring = what you want to change it to  Example: change/Table_Name/*/ –‘Table_Name’ is replaced with ‘*’

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/8 Copyright © 2004 Creating Tables  Some of the SQL-92 CREATE TABLE statements need to be modified for Oracle –Oracle does not support a CASCADE UPDATE constraint –Int data type is interpreted by Oracle as Number(38) –Varchar data type is interpreted as VarChar2 –Money or currency is defined in Oracle using the Numeric data type  Oracle sequences must be used for surrogate keys  DESCRIBE or DESC command is used to view table status

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/10 Copyright © 2004 Oracle Sequences  A sequence is an object that generates a sequential series of unique numbers  It is the best way to work with surrogate keys in Oracle  Two sequence methods –NextVal provides the next value in a sequence –CurrVal provides the current value in a sequence  Using sequences does not guarantee valid surrogate key values because it is possible to have missing, duplicate, or wrong sequence value in the table

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/11 Copyright © 2004 Example: Sequences  Creating sequence CREATE SEQUENCE CustID INCREMENT BY 1 START WITH 1000;  Entering data using sequence INSERT INTO CUSTOMER (CustomerID, Name, AreaCode, PhoneNumber) VALUES (CustID.NextVal, ‘Mary Jones’, ‘350’, ‘555–1234);  Retrieving the row just created SELECT * FROM CUSTOMER WHERE CustomerID = CustID.CurrVal

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/12 Copyright © 2004 DROP and ALTER Statements  Drop statements may be used to remove structures from the database –DROP TABLE MYTABLE; Any data in the MYTABLE table will be lost –DROP SEQUENCE MySequence;  ALTER statement may be used to drop (add) a column –ALTER TABLE MYTABLE DROP COLUMN MyColumn; –ALTER TABLE MYTABLE ADD C1 NUMBER(4);

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/13 Copyright © 2004 TO_DATE Function  Oracle requires dates in a particular format  TO_DATE function may be used to identify the format –TO_DATE(‘11/12/2002’,’MM/DD/YYYY’) 11/12/2002 is the date value MM/DD/YYYY is the pattern to be used when interpreting the date  TO_DATE function can be used with the INSERT and UPDATE statement to enter data –INSERT INTO T1 VALUES (100, TO_DATE (‘01/05/02’, ‘DD/MM/YY’);

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/14 Copyright © 2004 Creating Indexes  Indexes are created to –Enforce uniqueness on columns –Facilitate sorting –Enable fast retrieval by column values  Good candidates for indexes are columns that are frequently used with equal conditions in WHERE clause or in a join  Example: –CREATE INDEX CustNameIdx ON CUSTOMER(Name); –CREATE UNIQUE INDEX WorkUniqueIndex ON WORK(Title, Copy, ArtistID);

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/15 Copyright © 2004 Restrictions On Column Modifications  A column may be dropped at any time and all data will be lost  A column may be added at any time as long as it is a NULL column  To add a NOT NULL column –Add a NULL column –Fill the new column in every row with data –Change its structure to NOT NULL ALTER TABLE T1 MODIFY C1 NOT NULL;

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/16 Copyright © 2004 Creating Views  SQL-92 CREATE VIEW command can be used to create views in SQL*Plus  Oracle allows the ORDER BY clause in view definitions  Only Oracle 9i supports the JOIN…ON syntax  Example: CREATE VIEW CustomerInterests AS SELECT C.Name as Customer, A.Name as Artist FROM CUSTOMER C JOIN CUSTOMER_ARTIST_INT I ON C.CustomerID = I.CustomerID JOIN ARTIST A ON I.ArtistID = A.ArtistID;

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/17 Copyright © 2004 Enterprise Manager Console  The Oracle Enterprise Manager Console provides graphical facilities for managing an Oracle database  The utility can be used to manage –Database structures such as tables and views –User accounts, passwords, roles, and privileges  The Manager Console includes a SQL scratchpad for executing SQL statements

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/18 Copyright © 2004 Application Logic  Oracle database application can be processed using –Programming language to invoke Oracle DBMS commands –Stored procedures –Start command to invoke database commands stored in.sql files –Triggers

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/19 Copyright © 2004 Stored Procedures  A stored procedure is a PL/SQL or Java program stored within the database  Stored procedures are programs that can –Have parameters –Invoke other procedures and functions –Return values –Raise exceptions  A stored procedure must be compiled and stored in the database  Execute or Exec command is used to invoke a stored procedure –Exec Customer_Insert (‘Michael Bench’, ‘203’, ‘555- 2014’, ‘US’);

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/20 Copyright © 2004 Example: Stored Procedure  Insert Figure 10-20  IN signifies input parameters  OUT signifies an output parameter  IN OUT signifies a parameter used for both input and output  Variables are declared after the keyword AS

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/21 Copyright © 2004 Triggers  Oracle triggers are PL/SQL or Java procedures that are invoked when specified database activity occurs  Triggers can be used to –Enforce a business rule –Set complex default values –Update a view –Perform a referential integrity action –Handle exceptions

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/22 Copyright © 2004 Triggers (cont.)  Trigger types –A command trigger will be fired once per SQL command –A row trigger will be fired once for every row involved in the processing of a SQL command Three types of row triggers: BEFORE, AFTER, and INSTEAD OF BEFORE and AFTER triggers are placed on tables while INSTEAD OF triggers are placed on views Each trigger can be fired on insert, update, or delete commands

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/23 Copyright © 2004 Data Dictionary  Oracle maintains a data dictionary of metadata  The metadata of the dictionary itself are stored in the table DICT SELECT Table_Name, Comments FROM DICT WHERE Table_Name LIKE (‘%TABLES%’);  USER_TABLES contains information about user or system tables DESC USER_TABLES;

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/25 Copyright © 2004 Concurrency Control  Oracle processes database changes by maintaining a System Change Number (SCN) –SCN is a database-wide value that is incremented by Oracle when database changes are made  With SCN, SQL statements always read a consistent set of values; those that were committed at or before the time the statement was started  Oracle only reads committed changes; it will never reads dirty data

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/26 Copyright © 2004 Oracle Transaction Isolation  Oracle supports the following transaction isolation levels –Read Committed: Oracle’s default transaction isolation level since it never reads uncommitted data changes –Serializable: Dirty reads are not possible, repeated reads yield the same results, and phantoms are not possible –Read Only: All statements read consistent data. No inserts, updates, or deletions are possible –Explicit locks: Not recommended

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/27 Copyright © 2004 Oracle Security  Oracle security components: –An ACCOUNT is a user account –A PROFILE is a set of system resource maximums that are assigned to an account –A PRIVILEGE is the right to perform a task –A ROLE consists of groups of PRIVILEGEs and other ROLEs

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/28 Copyright © 2004 Account System Privileges  Each ACCOUNT can be allocated many SYSTEM PRIVILEGEs and many ROLEs  An ACCOUNT has all the PRIVILEGEs –That have been assigned directly –Of all of its ROLEs –Of all of its ROLEs that are inherited through ROLE connections  A ROLE can have many SYSTEM PRIVILEGEs and it may also have a relationship to other ROLEs  ROLEs simplify the administration of the database –A set of privileges can be assigned to or removed from a ROLE just once

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/29 Copyright © 2004 Account Authentication  Accounts can be authenticated by –Password –The host operating system  Password management can be specified via PROFILEs

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/30 Copyright © 2004 Oracle Recovery Facilities  Three file types for Oracle recovery: –Datafiles contain user and system data –ReDo log files contain logs of database changes OnLine ReDo files are maintained on disk and contain the rollback segments from recent database changes Offline or Archive ReDo files are backups of the OnLine ReDo files –Control files describe the name, contents, and locations of various files used by Oracle

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/31 Copyright © 2004 Oracle Recovery Facilities (cont.)  Oracle can operate in either ARCHIVELOG or NOARCHIVELOG mode –If running in ARCHIVELOG mode, Oracle logs all changes to the database –When the OnLine ReDo files fill up, they are copied to the Archive ReDo files  The Oracle Recovery Manager (RMAN) is a utility program used to create backups and to perform recovery

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/32 Copyright © 2004 Types of Failure  Oracle recovery techniques depend on the type of failure –An application failure due to application logic errors –An instance failure occurs when Oracle itself fails due to an operating system or computer hardware failure Oracle can recover from application and instance failure without using the archived log file –A media failure occurs when Oracle is unable to write to a physical file because of a disk failure or corrupted files The database is restored from a backup

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/33 Copyright © 2004 Oracle Backup Facilities  Two kinds of backups  A consistent backup: Database activity must be stopped and all uncommitted changes have been removed from the datafiles –Cannot be done if the database supports 24/7 operations  An inconsistent backup: Backup is made while Oracle is processing the database –An inconsistent backup can be made consistent by processing an archive log file

Fundamentals, Design, and Implementation, 9/e Chapter 10 Managing Databases with Oracle 9i Instructor: Dragomir R. Radev Winter 2005

Fundamentals, Design, and Implementation, 9/e KDD and Data Mining Instructor: Dragomir R. Radev Winter 2005

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/36 Copyright © 2004 The big problem  Billions of records  A small number of interesting patterns  “Data rich but information poor”

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/38 Copyright © 2004 Types of source data  Relational databases  Transactional databases  Web logs  Textual databases

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/39 Copyright © 2004 Association rules  65% of all customers who buy beer and tomato sauce also buy pasta and chicken wings  Association rules: X  Y

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/40 Copyright © 2004 Association analysis  IF 20 < age < 30 AND 20K < INCOME < 30K  THEN –Buys (“CD player”)  SUPPORT = 2%, CONFIDENCE = 60%

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/41 Copyright © 2004 Basic concepts  Minimum support threshold  Minimum confidence threshold  Itemsets  Occurrence frequency of an itemset

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/42 Copyright © 2004 Association rule mining  Find all frequent itemsets  Generate strong association rules from the frequent itemsets

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/43 Copyright © 2004 Support and confidence  Support (X)  Confidence (X  Y) = Support(X+Y) / Support (X)

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/44 Copyright © 2004 Example TIDList of item IDs T100I1, I2, I5 T200I2, I4 T300I2, I3 T400I1, I2, I4 T500I1, I3 T600I2, I3 T700I1, I3 T800I1, I2, I3, I5 T900I1, I2, I3

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/45 Copyright © 2004 Example (cont’d)  Frequent itemset l = {I1, I2, I5}  I1 AND I2  I5 C = 2/4 = 50%  I1 AND I5  I2  I2 AND I5  I1  I1  I2 AND I5  I2  I1 AND I5  I3  I1 AND I2

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/46 Copyright © 2004 Example 2 TIDdateitems T10010/15/99{K, A, D, B} T20010/15/99{D, A, C, E, B} T30010/19/99{C, A, B, E} T40010/22/99{B, A, D} min_sup = 60%, min_conf = 80%

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/47 Copyright © 2004 Correlations  Corr (A,B) = P (A OR B) / P(A) P (B)  If Corr < 1: A discourages B (negative correlation)  (lift of the association rule A  B)

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/48 Copyright © 2004 Contingency table Game^GameSum Video4,0003,5007,500 ^Video2,0005002,500 Sum6,0004,00010,000

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/49 Copyright © 2004 Example  P({game}) = 0.60  P({video}) = 0.75  P({game,video}) = 0.40  P({game,video})/(P({game})x(P({video })) = 0.40/(0.60 x 0.75) = 0.89

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/50 Copyright © 2004 Example 2 hotdogs^hotdogsSum hamburgers20005002500 ^hamburgers100015002500 Sum300020005000

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/51 Copyright © 2004 Classification using decision trees  Expected information need  I (s 1, s 2, …, s m ) = - p i log (p i )  s = data samples  m = number of classes 

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/52 Copyright © 2004 RIDAgeIncomestudentcreditbuys? 1<= 30HighNoFairNo 2<= 30HighNoExcellentNo 331.. 40HighNoFairYes 4> 40MediumNoFairYes 5> 40LowYesFairYes 6> 40LowYesExcellentNo 731.. 40LowYesExcellentYes 8<= 30MediumNoFairNo 9<= 30LowYesFairYes 10> 40MediumYesFairYes 11<= 30MediumYesExcellentYes 1231.. 40MediumNoExcellentYes 1331.. 40HighYesFairYes 14> 40Mediumnoexcellentno

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/53 Copyright © 2004 Decision tree induction  I(s 1,s 2 ) = I(9,5) = = - 9/14 log 9/14 – 5/14 log 5/14 = = 0.940

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/54 Copyright © 2004 Entropy and information gain E(A) = I (s 1j,…,s mj )  S 1j + … + s mj s Entropy = expected information based on the partitioning into subsets by A Gain (A) = I (s 1,s 2,…,s m ) – E(A)

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/55 Copyright © 2004 Entropy  Age <= 30 s 11 = 2, s 21 = 3, I(s 11, s 21 ) = 0.971  Age in 31.. 40 s 12 = 4, s 22 = 0, I (s 12,s 22 ) = 0  Age > 40 s 13 = 3, s 23 = 2, I (s 13,s 23 ) = 0.971

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/56 Copyright © 2004 Entropy (cont’d)  E (age) = 5/14 I (s11,s21) + 4/14 I (s12,s22) + 5/14 I (S13,s23) = 0.694  Gain (age) = I (s1,s2) – E(age) = 0.246  Gain (income) = 0.029, Gain (student) = 0.151, Gain (credit) = 0.048

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/58 Copyright © 2004 Other techniques  Bayesian classifiers  X: age <=30, income = medium, student = yes, credit = fair  P(yes) = 9/14 = 0.643  P(no) = 5/14 = 0.357

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/59 Copyright © 2004 Example  P (age < 30 | yes) = 2/9 = 0.222 P (age < 30 | no) = 3/5 = 0.600 P (income = medium | yes) = 4/9 = 0.444 P (income = medium | no) = 2/5 = 0.400 P (student = yes | yes) = 6/9 = 0.667 P (student = yes | no) = 1/5 = 0.200 P (credit = fair | yes) = 6/9 = 0.667 P (credit = fair | no) = 2/5 = 0.400

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/60 Copyright © 2004 Example (cont’d)  P (X | yes) = 0.222 x 0.444 x 0.667 x 0.667 = 0.044  P (X | no) = 0.600 x 0.400 x 0.200 x 0.400 = 0.019  P (X | yes) P (yes) = 0.044 x 0.643 = 0.028  P (X | no) P (no) = 0.019 x 0.357 = 0.007  Answer: yes/no?

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/61 Copyright © 2004 Predictive models  Inputs (e.g., medical history, age)  Output (e.g., will patient experience any side effects)  Some models are better than others

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/62 Copyright © 2004 Principles of data mining  Training/test sets  Error analysis and overfitting  Cross-validation  Supervised vs. unsupervised methods error input size training test

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/67 Copyright © 2004 kNN models  Assign each element to the closest cluster  Demos: –http://www- 2.cs.cmu.edu/~zhuxj/courseproject/knnd emo/KNN.html

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/68 Copyright © 2004 Other methods  Decision trees  Neural networks  Support vector machines  Demos –http://www.cs.technion.ac.il/~rani/LocBo ost/

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/69 Copyright © 2004 arff files @relation weather @attribute outlook {sunny, overcast, rainy} @attribute temperature real @attribute humidity real @attribute windy {TRUE, FALSE} @attribute play {yes, no} @data sunny,85,85,FALSE,no sunny,80,90,TRUE,no overcast,83,86,FALSE,yes rainy,70,96,FALSE,yes rainy,68,80,FALSE,yes rainy,65,70,TRUE,no overcast,64,65,TRUE,yes sunny,72,95,FALSE,no sunny,69,70,FALSE,yes rainy,75,80,FALSE,yes sunny,75,70,TRUE,yes overcast,72,90,TRUE,yes overcast,81,75,FALSE,yes rainy,71,91,TRUE,no

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/70 Copyright © 2004 Weka http://www.cs.waikato.ac.nz/ml/weka Methods: rules.ZeroR bayes.NaiveBayes trees.j48.J48 lazy.IBk trees.DecisionStump

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/71 Copyright © 2004 kMeans clustering  http://www.cc.gatech.edu/~dellaert/html/sof tware.html  java weka.clusterers.SimpleKMeans -t data/weather.arff

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/72 Copyright © 2004 More useful pointers  http://www.kdnuggets.com/  http://www.twocrows.com/booklet.htm

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/73 Copyright © 2004 More types of data mining  Classification and prediction  Cluster analysis (clustering)  Outlier analysis  Evolution analysis

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/75 Copyright © 2004 Methods  Single-linkage –One common pair is sufficient –disadvantages: long chains  Complete-linkage –All pairs have to match –Disadvantages: too conservative  Average-linkage  Centroid-based (online) –Look at distances to centroids

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/76 Copyright © 2004 k-means  Needed: small number k of desired clusters  hard vs. soft decisions  Example: Weka

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/77 Copyright © 2004 k-means 1 initialize cluster centroids to arbitrary vectors 2 while further improvement is possible do 3 for each document d do 4 find the cluster c whose centroid is closest to d 5 assign d to cluster c 6 end for 7 for each cluster c do 8 recompute the centroid of cluster c based on its documents 9 end for 10 end while

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/78 Copyright © 2004 Example  Cluster the following vectors into two groups: –A = –B = –C = –D = –E = –F =

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 10/79 Copyright © 2004 Demos  http://vivisimo.com/ http://vivisimo.com/  http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_h tml/AppletKM.html http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_h tml/AppletKM.html  http://cgm.cs.mcgill.ca/~godfried/student_projects/bonnef_k- means http://cgm.cs.mcgill.ca/~godfried/student_projects/bonnef_k- means  http://www.cs.washington.edu/research/imagedatabase/dem o/kmcluster http://www.cs.washington.edu/research/imagedatabase/dem o/kmcluster  http://www.cc.gatech.edu/~dellaert/html/software.html http://www.cc.gatech.edu/~dellaert/html/software.html  http://www-2.cs.cmu.edu/~awm/tutorials/kmeans11.pdf http://www-2.cs.cmu.edu/~awm/tutorials/kmeans11.pdf  http://www.ece.neu.edu/groups/rpl/projects/kmeans/ http://www.ece.neu.edu/groups/rpl/projects/kmeans/  % cd /data2/tools/weka-3-3-4 % export CLASSPATH=/data2/tools/weka-3-3-4/weka.jar % java weka.clusterers.SimpleKMeans -t data/weather.arff

Fundamentals, Design, and Implementation, 9/e Chapter 10 Managing Databases with Oracle 9i SII 654 Fall 2005.

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Fundamentals, Design, and Implementation, 9/e Chapter 10 Managing Databases with Oracle 9i SII 654 Fall 2005.

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