Extracting Peculiar Data from Multidatabases Using Agent Mining

  • Banu S
  • Saravanan V
  • Shriram R
Citations of this article
Mendeley users who have this article in their library.


Data mining is a broad term that describes the search to extract some meaningful information from data that is unformatted and either unstructured or partially structured Similarly, Fayyad et. al. described it as "The nontrivial process identifying valid, novel, potentially useful, and ultimately understandable patterns in data". Data mining is also known as knowledge discovery, knowledge extraction, information harvesting, data archeology, and data pattern processing. Although most algorithms provide some unique implementation of each phase, there are several common steps to achieve the goal of identifying patterns in data. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information-information that can be used to increase revenue, cuts costs, or both. This paper discusses the peculiar data mining and agent mining. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Keywords: Data mining peculiar mining, agent based system, multi agent .




Banu, S. S., Saravanan, V., & Shriram, R. (2013). Extracting Peculiar Data from Multidatabases Using Agent Mining. International Journal of Recent Technology and Engineering (IJRTE) (pp. 2277–3878).

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free