Application Areas Of Data Mining.
Hello friends, in this blog(Application Areas Of Data Mining) post we are going to discuss the application areas of data mining.
As we know data mining is a process of extracting useful information from huge or bulk data.|Application Areas Of Data Mining|
The discipline of data mining is driven in part by new applications that require new capabilities that are not concurrently being given by today’s technology. |Application Areas Of Data Mining|
These new applications can be naturally divided into three board categories.|Application Areas Of Data Mining|
Application Areas Of Data Mining:
Business And E-commerce Data:
it is a major source category of data for data mining applications.
Back-office, front-office, and network applications produce huge amounts of data about business processes.
Using this data for effective decision-making remains a fundamental challenge.
Business Transactions:
Modern business processes are consolidating with millions of customers and billions of their transactions.
Business enterprises require the necessary information for their effective functioning in today’s competitive world.
For example, they would like to know “Is that transaction fraudulent?”; “Which customer is likely to migrate?”, and “What product is this customer going to buy next?”
Electronic Commerce:
Not only does electronic commerce produce huge data sets in which the analysis of marketing patterns and risk patterns is…
… critical but, it is also important to do this in near-real time, to meet the demands of online transactions.
Scientific, Engineering, And Health Care Data:
Scientific data and metadata tend to be more difficult to structure than business data.
In addition, scientists and engineers are making increasing use of simulation and systems having application domain knowledge.
Genomic Data:
Genomic sequencing and mapping efforts have produced many databases that are accessible on the web.
In addition, there are also a wide variety of other online databases.
Searching for relationships between these data sources is another fundamental challenge for data mining.
Sensor Data:
Remote sensing data is another source of voluminous data.
Remote sensing satellites and a variety of other sensors produce huge amounts of geo-referenced data.
A fundamental challenge is to understand the relationships, including casual relationships, amongst this data.
simulation Data:
Simulation is now accepted as an important mode of science, supplementing theory and experiment.
Today, not only do experiments produce huge data sets, but so do simulations.
Data mining and, more generally, data-intensive computing have proved to be a critical link between theory, simulation, and experiment.
Healthcare Data:
Hospital healthcare organizations, insurance companies, and the concerned government…
…agencies accumulate huge collections of data about patients and healthcare-related details.
Understanding the relationship in which data is critical for a wide variety of problems ranging from determining what…
… procedures and clinical protocols are most effective, to how best to deliver health care to the maximum number of people.
Web Data:
The data on the web is growing not only in volume but also in complexity.
Web data now consists not only of text, audio, and video material but also of streaming data and numerical data.
Multimedia Documents:
Today’s technology for updating multimedia items on the web is far from satisfactory.
On the other hand, an increasingly large number of matters are on the web and the number of users also growing explosively.
It is becoming harder to extract meaningful information from the archives of multimedia data as the volume grows.
Data Web:
Nowadays the web is primarily oriented toward documents and their multimedia extensions.
HTML has proved itself to be an easy, yet powerful, language for supporting this.
Tomorrow, the potential exists for the web to prove equally important for working with data.
Extensible markup language(XML) is an emerging language for working with data in networked environments.
As this infrastructure grows, data mining is expected to be a critical enabling technology for the emerging data web.
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