Abstract
Nowadays, we are living in the midst of a data explosion and seeing a massive growth in databases so with the wide availability of huge amounts of data; necessarily we are become in need for turning this data into useful information and knowledge, where Data mining uncovers interesting patterns and relationships hidden in a large volume of raw data and big data is a new term used to identify the datasets that are of large size and have grater complexity. The knowledge gained from data can be used for applications such as market analysis, customer retention and production control. Data mining is a massive computing task that deals with huge amount of stored data in a centralized or distributed system to extract useful information or knowledge. In this paper, we will discuss Distributed Data Mining systems, approaches, Techniques and algorithms to deal with distributed data to discover knowledge from distributed data in an effective and efficient way.
Highlights
Data mining technology is used as a mode of identifying patterns and trends from big data, in other word the process of automatically discovering useful information in large datasets, Data mining uses artificial intelligence techniques, neural networks, and advanced statistical tools to detect trends, patterns, and relationships [1].Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge
User interface: This module communicates between users and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and performing exploratory data mining based on the intermediate data mining results
Distributed data mining algorithm is classified into three classes [8]: 1. Distributed Data Mining (DDM) based on Multi Agent System
Summary
Data mining technology is used as a mode of identifying patterns and trends from big data, in other word the process of automatically discovering useful information in large datasets, Data mining uses artificial intelligence techniques, neural networks, and advanced statistical tools to detect trends, patterns, and relationships [1]. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration [2]. Some features of distributed scenario where Distributed Data Mining are applicable are: A system consists of multiple independent sites of data and computation which communicate only through message passing, Communication between sites is expensive, and Sites have resource and privacy concerns. The primary goal of many distributed data mining method is to reduce the number of messages send. The paper is organized as follows: Section. we describe data mining and architecture of a typical data mining system,Section. we offer knowledge discovery in database and stages of KDD process and in section. we offer data mining techniques such as classification clustering. Etc and section. and section. we show general view about distributed data mining systems and its architecture, in section. we show steps in Distributed data mining; And in section. offer Distributed data mining algorithm
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
