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Origins of data mining and Data Mining tasks?

Origins of data mining and Data Mining tasks


Asked On2017-05-17 06:48:47 by:pallaviaithaln

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Origins of datatmining

Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems Traditional Techniques may be unsuitable due to 

 Enormity of data Statistics/ Machine Learning/ AI Pattern 

 High dimensionality Recognition of data 

 Heterogeneous, Data Mining distributed nature of data Database systems

Answerd on:2016-03-08 Answerd By:metaphor

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We have observed various types of databases and information repositories on which data mining can be performed.
Let us now examine the kinds of data patterns that can be mined.
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 perform inference on the current data in order to make predictions.
In some cases, users may have no idea regarding what kinds of patterns in their data may be
interesting, and hence may like to search for several different kinds of patterns in parallel.
Thus it is important to have a data mining system that can mine multiple kinds of patterns to
accommodate different user expectations or applications. Furthermore, data mining systems
should be able to discover patterns at various granularity (i.e., different levels of abstraction).
Data mining systems should also allow users to specify hints to guide or focus the search for interesting patterns. Because some patterns may not hold for all of the data in the database, a measure of certainty or trustworthiness is usually associated with each discovered pattern.
Data mining functionalities, and the kinds of patterns they can discover, are described below.

Concept/Class Description: Characterization and Discrimination
Data can be associated with classes or concepts. For example, in the AllElectronics
store, classes of items for sale include computers and printers, and concepts of customers include bigSpenders
and budgetSpenders.
It 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 (1)
data characterization, by summarizing
the data of the class under study (often called the target class) in general terms, or (2) data discrimination
, by comparison of the target class with one or a set of comparative classes
(often called the contrasting classes), or (3) both data characterization and discrimination.
Data characterization is a summarization of the general characteristics or features of a target class of data.
The data corresponding to the user-specified class are typically collected by a database query.
For example, to study the characteristics of software products whose sales increased by 10% in the last year, the data related to such products can be collected by executing
an SQL query.


Answerd on:2015-01-20 Answerd By:pallaviaithaln

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