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PRACTICAL No. 02

AIM:What are relations? How can you create in any RDBMS?Ans: The case in which data in one table is related to the data inanother table. This connection between two tables is called a relation.

We can create relations in any RDB MS such that when there is such arelation between two tables, these tables will be connected by insertingthe Primary Key of one table into the corresponding row of the othertable. The field such a way to connect the two tables is known as

Foreign Key.

These relations exists as follows: One-to-one One-to-many Many-to-one Many-t- many

a) Write a query for table creation in any DBMS and create a tableaccordingly having minimum seven fields and 10 records.

Query:

CREATE TABLE STUDENT_INFO(First_Name nvarchar(25) Not Null,Middle_Name nvarchar(10),Last_Name nvarchar(25),Enrollment_No numeric(18,0) not null,Gender char(1) not null,Date_of_Birth nchar(15) not null,Mobile numeric(20),Address nvarchar(50));

Prerana Tokas MCA-II | 02016204411 3

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b) Insert data in the above created table.

Query:

INSERT INTO STUDENT_INFO VALUES('Shweta','U','Singh',00416204411,,F,'16-April-1991',9711512707,'Subroto Park');

INSERT INTO STUDENT_INFO VALUES('Prerana','A','Tokas',00516204411,,F,'04-Feb-1991',9711512787,'Munirka');

INSERT INTO STUDENT_INFO VALUES('Jyoti','P','Yadav',00716204411,,F,'21-Nov-1994',8654512707,'Subroto Park');

c) Retrieve the data stored in the table on various criteria.

Query:

i. Select * from STUDENT_INFO;

ii. Select Count(*) as 'Total Records in the table' fromSTUDENT_INFO;

iii. Select * from STUDENT_INFO WHEREEnrollment_No='00416204411';

iv. Select First_Name, Moblie_No from STUDENT_INFOWHERE Address='Subroto Park';

d) Create any appropriate view based on that table.

Query:

CREATE VIEW STUDENT_VIEW ASSELECT First_Name, Enrollment_NoFROM STUDENT_INFOWHERE Gender='F';


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MS-Excel


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PRACTICAL No. 03

Create a pivot table of OLAP technology using MS-Excel.

Open->MS ExcelGo to Data Import Database Select ColumnsInsert Pivot table


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PRACTICAL No. 04

Demonstrate Slice-and-Dice concept of OLAP technology.

Open->MS ExcelGo to Data Import Database Select ColumnsInsert Pivot table


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PRACTICAL No. 05

Demonstrate Drill-down and Roll-up concept of OLAP technologyusing MS-Excel.

Drill Down:


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Rollup


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PRACTICAL No. 06

Demonstrate rotation concept of OLAP technology using pivot table in

MS-Excel.


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PRACTICAL No. 07

Demonstrate scenario manager in MS-Excel.

Select a data fieldGo to Data menu Click What-if Analysis Open Scenario Manager Make changes in the data field using Scenario Manager and displaysummary.

Values of the data field after change.


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Scenario Summary:


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PRACTICAL No. 08

Demonstrate goal seek in MS-Excel.

Select a cell Create a formulaGo to Data menu Click What-if Analysis Open Goal Seeker Observe the goals that are achieved after using Goal Seeker

Sheet with formula:


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Goal achieved by Goal-Seeker


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WEKA 3.6


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PRACTICAL No. 09

We are going to work with the Data provided in Weka. We are going to work withAgrawal database.


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Perform classification task of data mining using Weka Explorer.

Choose ClassifySelect classifier as Decision Table and start generating the classifications as per age:

The screenshot is shown below:


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PRACTICAL No. 10

Perform clustering task of data mining using Weka Explorer.

Choose-> Cluster Choose Clusterer as HierarchicalClustererSelect Classes to cluster evaluation as groups


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PRACTICAL No. 11

Perform Association task of data mining using Weka Explorer.

Choose AssociateSelect Associator as FilteredAssociator


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PRACTICAL No. 12

Perform classification task of data mining using Weka Knowledge Flow.

Open KnowledgeFlow Load DataSourceConnect various componentsStart loading


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Classification result using Decision Tree:

PRACTICAL No. 13


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Perform Association task of data mining using Weka Knowledge Flow.


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MATLAB

PRACTICAL No. 15


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Show the computation of A*b, a+b, b-5, A*a'

A*b:

a+b


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PRACTICAL No. 16


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Create a MATLAB program to display the table of the num entered bythe user.

PRACTICAL No. 17


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Create a MATLAB program to display the factorial of the numentered by the user.

PRACTICAL No. 18


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Create a MATLAB program to display the odd num within limitentered by the user.

PRACTICAL No. 19


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Create a graph using MATLAB built in functions.

plot:


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ezplot:

fplot with grid:


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PRACTICAL No. 20

Perform classification in MATLAB.


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Classification

Suppose we have a data set containing observations with measurements on differentvariables (called predictors) and their known class labels. If we obtain predictor valuesfor new observations, could we determine to which classes those observations probably

belong? This is the problem of classification. This illustrates how to perform someclassification algorithms in MATLAB by applying them to Fisher's iris data.

Contents:

Fisher's Iris Data Linear and Quadratic Discriminant Analysis NaiveBayes Classifier Decision Tree

Code:load fisheririsgscatter(meas(:,1), meas(:,2), species,'rgb','osd');xlabel('Sepal length');ylabel('Sepal width');

N = size(meas,1);

PRACTICAL No. 21

Perform clustering in MATLAB.

http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#1http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#3http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#15http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#19http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#3http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#15http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#19http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#1

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Cluster Analysis

Cluster analysis is a way to examine similarities and dissimilarities of observations or objects. Data often fall naturally into groups, or clusters, of observations, where thecharacteristics of objects in the same cluster are similar and the characteristics of objectsin different clusters are dissimilar.

Contents:

K-Means and Hierarchical Clustering Fisher's Iris Data Clustering Fisher's Iris Data Using K-Means Clustering Clustering Fisher's Iris Data Using Hierarchical Clustering

Code:load fisheriris[cidx2,cmeans2] = kmeans(meas,2,'dist','sqeuclidean');[silh2,h] = silhouette(meas,cidx2,'sqeuclidean');
http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#1http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#3http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#4http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#17http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#1http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#3http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#4http://www.mathworks.in/products/statistics/examples.html?file=/products/demos/shipping/stats/clusterdemo.html#17

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