19-03-2012, 03:40 PM
DATA MINING and WAREHOUSING
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What is data mining?
Data mining refers to extracting or “mining” knowledge from large amounts
of data or historical data.knowledge mining from data, knowledge extraction,
data/pattern analysis, data archaeology, and data dredging.
Many people treat data mining as a synonym for another popularly used term, Knowledge
Discovery from Data, or KDD. Alternatively, others view data mining as simply an essential step in the process of knowledge discovery. Knowledge discovery as a process
In this step in process, data evolution is presented.At scratch data is maintained in ERA.after it is stored in flatfiles&spread sheets,because of sorting ,searching,fast retrievals DBMS is invented.but DBMS is restricted to maintin single Database only.So the Datawarehouse is Invented.after Data mining is defined to extract data from data warehouse.
it consists of an iterative sequence of the following steps:
1. Data cleaning (to remove noise and inconsistent data)
2. Data integration (where multiple data sources may be combined)1
3. Data selection (where data relevant to the analysis task are retrieved fromthe database)
4. Data transformation (where data are transformed or consolidated into forms appropriate
for mining by performing summary or aggregation operations, for instance)2
5. Data mining (an essential process where intelligent methods are applied in order to
DATA MINING FUNCTIONALITIES
Data mining functionalities are used to specify the kind of patterns to be found in Data mining Tasks. In general, data mining tasks can be classified into two categories:
descriptive and predictive. Descriptive mining tasks characterize the general properties
of the data in the database. Predictive mining tasks performinference on the current data
in order to make predictions.
Data mining functionalities, and the kinds of patterns they can discover, are described
below.
Data Characterization:-
Data can be associated with classes or concepts. For example, in the AllElectronics store,
classes of items for sale include computers and printersIt can be useful to describe individual classes and concepts
in summarized, concise, and yet precise terms. Such descriptions of a class or
a concept are called class/concept descriptions. These descriptions can be derived via
data characterization, by summarizing the data of the class under study (often called
the target class) in general terms, or data discrimination, by comparison of the target
class with one or a set of comparative classes (often called the contrasting classes).
Data discrimination :-
Data discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes. The target and contrasting classes can be specified by the user, and the corresponding data objects retrieved through database queries.Discrimination descriptions expressed in rule form are referred to asdiscriminant rules.
example:-
A data mining system should be able to compare two groups of AllElectronics customers, such as those who shop for computer products regularly (more than two times a month) versus those who rarely shop for such products (i.e., less than three times a year). The resulting description provides a general comparative profile of the customers, such as 80% of the customers who frequently purchase computer products are between 20 and 40 years old and have a university education, whereas 60% of the customers who infrequently buy such products are either seniors or youths, and have no university degree. Drilling down on a dimension, such as occupation, or adding new dimensions, such as income level, may help in finding even more discriminative features between the two classes.
Assosiation analysis:-
Assosiation analysis is used to find freaquenlty occuring patterns.Mining frequent patterns leads to the discovery of interesting associations and correlations within data.output of the assosiation analsis is presented in terms of rules