Data Mining Algorithms and Techniques

Data Mining Algorithms and Techniques

Ambika P.
Copyright: © 2018 |Pages: 14
DOI: 10.4018/978-1-5225-5972-6.ch010
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Abstract

Integration of data mining tasks in day-to-day life has become popular and common. Everyday people are confronted with opportunities and challenges with targeted advertising, and data mining techniques will help the businesses to become more efficient by reducing processing cost. This goal of this chapter is to provide a comprehensive review about data mining, data mining techniques, popular algorithms, and their impact on fog computing. This chapter also gives further research directions on data mining on fog computing.
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Introduction

Development of IoT and the prevalence of ubiquitously connected smart devices are main source of computing. Computing paradigms process big data which categorize rapidly generated, wide variety of huge volumes of data. Gartner – technology research and advisory corporate said nearly 26 billion devices will be connected in the Internet of Things by 2020. Intelligent data mining and analysis plays a key role to achieve benefits in the following fields: Business, medicine, science and engineering etc.,

What Is Data Mining?

Data mining is a method of analysing data and finding out new patterns from large data set. Data mining contains lot of definitions from different authors Agrawal et al. (1991), Han & Yu(1996), Zomya et al(1999) and Jong (1995). In other words, it is a convenient way of extracting useful knowledge from the massive data sets like trends, patterns and rules. In recent days, large amount of data is created from multiple sources and mining tasks focus on issues related to feasibility, usefulness, scalability and effectiveness. Data mining is a process of Knowledge Discovery from Data which extracts implicit, previously unknown and useful patterns or rules from historical data (Jiawei, Kamber & Pei, 2012).

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