This post is part of Series Business Intelligence – Tools & Theory
Currently running topic for this series is listed as below :
Series Business Intelligence – Tools & Theory
>>Chapter 1 : Business Intelligence an Introduction
>>Chapter 2 : Business Intelligence Essentials
>>Chapter 3 : Business Intelligence Types
>>Chapter 4 : Architecting the Data
>>Chapter 5 : Introduction of Data Mining<You are here>
Continuing from my previous post on this series, If you have missed any link please visit link below
We are going to Cover the Following Points in this article
- Functionalities of Data Mining
- Classification on Data Mining system
Functionalities of Data Mining
We leant in the previous section the kinds of data mining.
This section deals with the functionalities on data mining. The kinds of patterns that can be discovered depend upon the data mining tasks employed.
The data mining functionalities are briefly presented in the following lists:
· Characterization: This is a review of common features of matter in an object class and is called as characteristics rules. The data appropriate to a user-specified class are usually retrieved by a database inquiry and levels of concepts.
Example: One may desire to characterize the Mega Video Store customers who frequently rent more than 30 movies a year. With model hierarchies on the attributes relating the target class the attribute orientation method3 can be used.
· Discrimination: Data discrimination produces discrimination rules and is on the whole the contrast of the general characters of object between two classes referred to as the target class and the distinct class. For example, one may want to evaluate the general character of the customers who rented more than 30 movies in the last year with those whose hiring account is lower than 5. The techniques used for data discrimination are very similar to the practice used for data characterization with the exemption that data discrimination results include comparative measures.
· Association analysis: It is also called association rules. It revises the occurrence of items occurring together in transactional databases, and based on access called support, which classify the frequent item sets. For example, it could be useful for the Mega Video Store manager to know what movies are often rented together or if there is a relationship between renting a certain type of movies and buying popcorn.
· Prediction: It has possible conclusion of successful forecasting in a business context. There are two major types of calculations: one can either try to forecast some occupied data values or pending trends, or forecast a class tag for some data. The latter is attached to classification. Once a classification model is built based on a training set, the class tag of an object can be foreseen based on the attribute values of the object and the attribute values of the classes.
Classification on Data Mining system
A data mining system is methodical approaches to collect, organize, and analyze data sets. Data mining is the union of a set of regulation, which comprises of database systems, statistics, mechanism knowledge, and visualization and information discipline. They are as follows:
· Classification according to the kinds of databases mined.
A data mining system can be categorized according to the varieties of databases mined. It can be categorized according to diverse criterion (such as data models, or the types of data or functions involved), each of which may possess data mining system.
For example, if classifying according to data models, we may have a
relational, operational, object-oriented, object, data warehouse mining system. If classifying according to the particular types of data handled, we may have time-series, text, multimedia data mining system, or a World-Wide Web mining system.
· Classification according to the kinds of knowledge mined.
Data mining systems can be classified according to the kinds of knowledge they extract, that is, based on data mining gathering, such as characterization, biases, involvement, classification, grouping, and progress analysis, variation analysis, similarity analysis, etc.
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