Ad
related to: data mining tasks ppt- How Freelancer Works
Post Your Project, Choose a
Freelancer, Get Your Job Done!
- Post Your Project - Free
Post Your Project & Receive Bids
From Freelancers Within Seconds.
- Outsource Anything
Need Something Done? Hire a
Professional in Any Field, Today.
- Get The Perfect Design
Start a Contest Today - 100s of
Entries, Only Pay for the Best!
- Sign Up Now - Free
Join the World's Largest
Freelancing Platform Today.
- Our Team is Here to Help
Let Our Recruiters Find The
Perfect Freelancer For Your Project
- How Freelancer Works
Search results
Results From The WOW.Com Content Network
The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).
The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.
The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured, and unstructured data sources.
Metabolomics is a very data heavy subject, and often involves sifting through massive amounts of irrelevant data before finding any conclusions. Data mining has allowed this relatively new field of medical research to grow considerably within the last decade, and will likely be the method of which new research is found within the subject. [28]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Cluster analysis, a fundamental task in data mining and machine learning, involves grouping a set of data points into clusters based on their similarity. k-means clustering is a popular algorithm used for partitioning data into k clusters, where each cluster is represented by its centroid.
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." [1] Written resources may include websites, books, emails, reviews, and ...
Wrapper in data mining is a procedure that extracts regular subcontent of an unstructured or loosely-structured information source and translates it into a relational form, so it can be processed as structured data. [1] Wrapper induction is the problem of devising extraction procedures on an automatic basis, with minimal reliance on hand ...