data mining n.
Skip this Video
Download Presentation
Data Mining

Loading in 2 Seconds...

play fullscreen
1 / 19

Data Mining - PowerPoint PPT Presentation

  • Uploaded on

Data Mining. By: Thai Hoa Nguyen Pham. Data Mining. Define Data Mining Classification Association Clustering. Define Data Mining. Also known as KDD (Knowledge-Discovery in Database). Data mining is the semiautomatic process of analyzing data to find useful patterns. Why semiautomatic?

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Data Mining' - morey

Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
data mining

Data Mining

By: Thai Hoa Nguyen Pham

data mining1
Data Mining
  • Define Data Mining
  • Classification
  • Association
  • Clustering
define data mining
Define Data Mining
  • Also known as KDD (Knowledge-Discovery in Database).
  • Data mining is the semiautomatic process of analyzing data to find useful patterns.
  • Why semiautomatic?

Manual preprocessing of data and postprocessing of data.

examples of data mining
Examples of Data Mining
  • A simple example would be of a clothing retail store. A data mining system could be used to list the customers who often buy t-shirts during the Summer season.
  • Another example would be of the urban legend of how Walmart used data mining to find a correlation between customers buying beer and baby diapers. So they put the two aisles close together to increase profits.
  • If it is given that items in databases are put into classes, a problem arises when a new item wants to be added to the database.
  • The class for the new item is unknown, so other methods have to be used to find the right class for the item to be put in. Rules then come in to solve the problems.
example of a rule
Example of a rule

P, = masters and P.income > 75,000 => = excellent

P, = bachelors and P.income < 50K => = bad

decision tree classifiers
Decision Tree Classifiers
  • Widely used technique for classification.
  • Internal nodes either called functions or predicates
  • Leaf nodes are associated classes.
example of decision tree classifiers
Example of Decision Tree Classifiers

Root 

Functions 

Classes 

example of decision tree classifiers1
Example of Decision Tree Classifiers
  • Internal nodes or functions are inside the boxes—degree (root) and income.
  • Leaf nodes or associated classes are the four different circles—bad, average, good, excellent.
  • An example of an association for beer and diapers would be:

Beer => Diapers

  • As already mentioned, the above association just means that customers that buy beer often buy diapers, too.
association rules
Association Rules
  • Support—is a measure of what fraction of the population satisfies both the antecedent and the consequent. In other words, in the association below:

milk => screwdrivers

Higher percentage of the above association happening is worth more attention than lower percentage.

association rule 2
Association Rule 2
  • Confidence– The measure of how often the consequent is true when the antecedent is true.

bread = > milk

For example, if the association above had a confidence of 50 percent, it just means that 50 percent of the purchases include bread and milk, but it leaves room for other items purchased with the bread.

  • Clustering refers to finding clusters of points in a given data and grouping them in different subsets.
  • Widely used clustering techniques—Hierarchical clustering, agglomerative clustering, and divisive clustering.
types of clustering
Types of Clustering
  • Hierarchical—clustering that deals with grouping things by importance.
  • Agglomerative—start by building small clusters, then progressively merge into larger clusters.
  • Decisive—begins with whole set and successively divides into smaller clusters.
example of agglomerative hierarchical clustering
Example of agglomerative hierarchical clustering

An example of a agglomerative clustering, where we have separate elements of a set merging with each internal node until the last merge “abcdef” is achieved.

other types of mining
Other types of mining
  • Text Mining– data mining techniques to textual documents. An example would be how there is a tool to form clusters on pages that users have visited. So if a user supplies a site and defines that he/she wants a site containing the keyword “Japan”, a list of sites that used the keyword “Japan” the most will appear.
  • Data Visualization—helps users to examine large volumes of data, and to detect patterns visually. So instead of seeing problems through text, visual displays can use maps and charts to pinpoint where the problem is with some color coding scheme.
example of text mining
Example of Text Mining

This example shows what happens when a user does a search for “Japan”. The points closer to the center of the circle has more information on Japan. We can think of the points as websites or research articles.

example of data visualization
Example of Data-visualization

We could say a number of things for this example. We could say the map depicts poverty levels or which state grows more apples.

  • Data mining. (2006, October 27). In Wikipedia, The Free Encyclopedia. Retrieved 05:59, October 30, 2006, from
  • Data clustering. (2006, October 29). In Wikipedia, The Free Encyclopedia. Retrieved 06:03, October 30, 2006, from
  • GISmatters (2004-2006) Retrived on October 31, 2006, from

  • Martin, G., Spath, J. (2000) Kryptasthesie. Retrieved on October 31, 2006 from

  • Silberschaz, A., Korth, H., Sudarshan, S. (2002). Database System Concepts. New York: New York.