Research Article Open Access

Knowledge Discovery Process: The Next Step for Knowledge Search

Abstract

The process of extracting knowledge from the large volumes of data is data mining is a crucial step in KDD (Knowledge Discover from Data). Many algorithms are available to analysis data in mining. But process of getting knowledge was not explained in detail. We attempt to have a process to find new knowledge. KDP (Knowledge Discovery Process) is defined as the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. The process generalizes to non database sources of data, although it emphasizes databases as a primary source of data. It consists of many steps (one of them is DM), each attempting to complete a particular discovery task and each accomplished by the application of a discovery method. Knowledge discovery concerns the entire knowledge extraction process, including how data are stored and accessed, how to use efficient and scalable algorithms to analyse massive datasets, how to interpret and visualize the results, and how to model and support the interaction between human and machine. It also concerns support for learning and analysing the application domain. Although the models usually emphasize independence from specific applications and tools, they can be broadly divided into those that take into account industrial issues and those that do not. However, the academic models, which usually are not concerned with industrial issues, can be made applicable relatively easily in the industrial setting and vice versa. We restrict our discussion to those models that have been popularized in the literature and have been used in real knowledge discovery projects

Ravindra Changala, D.Rajeswara Rao, T Janardhana Rao, P Kiran Kumar, Kareemunnisa

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