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布里斯班代寫assignment

Business Intelligence代寫(COIS 13013)

 

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Business Intelligence(COIS 13013)
Assignment 2
 
9/28/2012
 
Suraj Humagain (S0202096)
 
 
 

 
Question 1
The three main areas of Web mining are as follows :
a.       Web Content Mining :  Actually Web Content Mining is the scanning and mining of text, pictures and graphs of a Web page to determine the relevance of the content to the search query.
b.      Web Structure Mining : Web Structure Mining  is  a tool used to identify the relationship between Web pages linked by information or direct link connection.
c.       Web Usage Mining :  This type of web mining allows for the collection of Web access information for Web pages. This usage data provides the paths leading to accessed Web pages
 
There are lots of difference which we can find in between these areas like  Web content mining is the scanning  and mining of pictures and graphs whereas Web structure mining  is a tool  which identify relationship between web pages and Web Usage mining  allows for the collection of Web access information  which provides path and leads to access Web pages.
Question 2:
Database is a structured collection of records or data that is stored in a computer system and the structure is achieved by organizing the data according to a database model whereas data warehouse is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis and it is used to retrieve and analyse data, to extract, transform and load data, and to manage the data dictionary. Database is used for Online Transactional Processing (OLTP) but can be used for other purposes such as Data Warehousing and this records the data from the user for history whereas data warehouse Used for Online Analytical Processing (OLAP) and this reads the historical data for the Users for business decisions. The tables and joins are complex since they are normalized in database whereas the Tables and joins are simple since they are de-normalized in data warehouse. Database is optimized for write operation whereas data warehouse is optimized for read operations.
   The major benefits for end users to use the data warehouse are:
a.       They store and present information in such a way that it allows business executives to make important decisions.
b.      You can modify or enhance the data without affecting the core system.  For example you could rename fields to make them more meaningful, or attach comments to information that may not be supported in the core system
c.       Improved processing speed, independence, and reducing or preventing record locking or other issues.
d.       You can control when data is synchronized with the core systems.  Therefore you can schedule this to occur “after hours” or during periods of reduced demand on the core system.
e.       You can usually reduce license fees when some of your users only need to view information that is captured and stored in a data warehouse versus the core system that might have much more expensive licensing requirements.
If I was given a task to select a data warehouse vendor for the company, the criteria I will take into consideration for the task are:
a.       Does the vendor provide tool training services?
b.      What references can the vendor provide that indicate the successful use of the tool on other data warehousing implementations?
c.       Is the tool generic rather than specific to data warehousing?
d.      Does the vendor provide tool consulting services?
e.       Does the vendor sell delivery services rather than selling its tools?
 
 
 
Question 3:
WEKA is one of the popular data mining tools known as the Waikato Environment for Knowledge Analysis that contains tool for data pre-processing, classification, regression, clustering, association rules and visualization. Weka is an open source data mining tool that supports data mining algorithms bur also data preparation and Meta learners like bagging and boosting.
The two stable versions of Weka for windows can be found. The new versions come with the GUI that provides the users with more flexibility than the command lines.
The Weka can be started by clicking the windows start button. To open the GUI version of Weka following three step procedures should be followed:
1.      The first step is to click on the start menu and choose the WEKA and select the WEKA.

Fig-1: Weka GUI version start up
 
2.      Click on the explorer option to open the diabetes and click on the open file tab to get the diabetes data from the original save place. The data file is already available in the Weka data directory in ARFF format. After the file is open it will come up with different option on the screen providing some information about the diabetes data, such as the number of instances, the number of missing values, the number of attributes and also statistical information about the attributes one at a time . The screen is shown below:

 
Fig-2: Quantitative and graphical explanation of the data file
 
 
Classifying the data
For the classification of the data, the first step is to click on the classify button on the main GUI and then click option choose after that a pop up screen will show the folder name tree and select J48.
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Fig: J48 classifier inside tree
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                   
 
 
 
 

Fig:       Classifier output with cross validation
 
 
 
 
After the selection of the attributes click start button as shown on the figure to begin the classifier which will display the result as below:
 
 
 
 
 
 
 
=== Run information ===
 
Scheme:       weka.classifiers.trees.J48 -C 0.25 -M 2
Relation:     pima_diabetes
Instances:    768
Attributes:   9
              preg
              plas
              pres
              skin
              insu
              mass
              pedi
              age
              class
Test mode:    10-fold cross-validation
 
=== Classifier model (full training set) ===
 
J48 pruned tree
------------------
 
plas <= 127
|   mass <= 26.4: tested_negative (132.0/3.0)
|   mass > 26.4
|   |   age <= 28: tested_negative (180.0/22.0)
|   |   age > 28
|   |   |   plas <= 99: tested_negative (55.0/10.0)
|   |   |   plas > 99
|   |   |   |   pedi <= 0.561: tested_negative (84.0/34.0)
|   |   |   |   pedi > 0.561
|   |   |   |   |   preg <= 6
|   |   |   |   |   |   age <= 30: tested_positive (4.0)
|   |   |   |   |   |   age > 30
|   |   |   |   |   |   |   age <= 34: tested_negative (7.0/1.0)
|   |   |   |   |   |   |   age > 34
|   |   |   |   |   |   |   |   mass <= 33.1: tested_positive (6.0)
|   |   |   |   |   |   |   |   mass > 33.1: tested_negative (4.0/1.0)
|   |   |   |   |   preg > 6: tested_positive (13.0)
plas > 127
|   mass <= 29.9
|   |   plas <= 145: tested_negative (41.0/6.0)
|   |   plas > 145
|   |   |   age <= 25: tested_negative (4.0)
|   |   |   age > 25
|   |   |   |   age <= 61
|   |   |   |   |   mass <= 27.1: tested_positive (12.0/1.0)
|   |   |   |   |   mass > 27.1
|   |   |   |   |   |   pres <= 82
|   |   |   |   |   |   |   pedi <= 0.396: tested_positive (8.0/1.0)
|   |   |   |   |   |   |   pedi > 0.396: tested_negative (3.0)
|   |   |   |   |   |   pres > 82: tested_negative (4.0)
|   |   |   |   age > 61: tested_negative (4.0)
|   mass > 29.9
|   |   plas <= 157
|   |   |   pres <= 61: tested_positive (15.0/1.0)
|   |   |   pres > 61
|   |   |   |   age <= 30: tested_negative (40.0/13.0)
|   |   |   |   age > 30: tested_positive (60.0/17.0)
|   |   plas > 157: tested_positive (92.0/12.0)
 
Number of Leaves  :      20
Size of the tree :        39
 
 
Time taken to build model: 0.06 seconds
 
=== Stratified cross-validation ===
=== Summary ===
 
Correctly Classified Instances         567               73.8281 %
Incorrectly Classified Instances       201               26.1719 %
Kappa statistic                          0.4164
Mean absolute error                      0.3158
Root mean squared error                  0.4463
Relative absolute error                 69.4841 %
Root relative squared error             93.6293 %
Total Number of Instances              768    
 
=== Detailed Accuracy By Class ===
 
TP Rate   FP Rate   Precision   Recall  F-Measure   Class
  0.814     0.403      0.79      0.814     0.802    tested_negative
  0.597     0.186      0.632     0.597     0.614    tested_positive
 
=== Confusion Matrix ===
 
   a   b   <-- classified as
 407  93 |   a = tested_negative
 108 160 |   b = tested_positive
 
 
 
According to the data process shown above there were 768  instances. The number of leaves was 20 while the size of the tree was 39. Out of total 768 instances only 567 instances were correctly classified that is 73.821 %. The remaining 201 instances were incorrectly classified that is 26.1719 % of the total instances. The statistics of detailed accuracy and a confusion matrix are also generated
 
 
 
Question 4:
The major characteristics of artificial intelligence are:
  • The ability to act intelligently, as a human.
  • The ability to behave following "general intelligent action."
  • The ability to artificially simulate the human brain.
  • The ability to actively learn and adapt as a human.
  • The ability to process language and symbols.
Examples:
a.       Every video game where you play a computer, uses AI
b.      AI's are being used to analyze data. Be it scientific data from which equations must be extracted, tons of financial data to try to predict stock markets or web pages to get better search results.
 
 
 
 
 
 
 
 
 
 

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