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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 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.
Classification 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
P, P.degree = masters and P.income > 75,000 => P.credit = excellent
P, P.degree = bachelors and P.income < 50K => P.credit = bad
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 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.
Association 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 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
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 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 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
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 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
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
We could say a number of things for this example. We could say the map depicts poverty levels or which state grows more apples.
References Data mining. (2006, October 27). In Wikipedia, The Free Encyclopedia. Retrieved
05:59, October 30, 2006, from http://en.wikipedia.org/w/index.php?title=Data_mining&oldid=84059363
Data clustering. (2006, October 29). In Wikipedia, The Free Encyclopedia.
Retrieved 06:03, October 30, 2006, from http://en.wikipedia.org/w/index.php?title=Data_clustering&oldid=84478616
GISmatters (2004-2006) Retrived on October 31, 2006, from http://www.gismatters.com/over65.html
Martin, G., Spath, J. (2000) Kryptasthesie. Retrieved on October 31, 2006 from http://www.projekttriangle.com/work/work_rwe.htm?research
Silberschaz, A., Korth, H., Sudarshan, S. (2002). Database System Concepts. New York: New York.