Big Data Analysis and Mining
Advancements in the field of information science and technology enables users to collect or generate high volumes of valuable data of different levels of veracities such as streams of banking, financial, and shopper market basket data at high velocities from wide varieties of data source in various reallife business, engineering, scientific applications in modern organizations and society. Embedded in these big data (Madden, 2012; Leung, 2015) is implicit, previously unknown, and potentially useful information and knowledge. However, these big data come with volumes beyond the ability of commonly used software to capture, manage, and process within a tolerable elapsed time. Hence, new forms of information science and technology such as big data analysis and mining are needed to process and analyze these big data so to as enable enhanced decision making, insight, knowledge discovery, and process optimization. Over the past few years, algorithms have been proposed for various big data analysis and mining tasks, including clustering (which groups similar data together) classification (which categorizes groups of similar data) outlier detection (which identifies anomalies) and frequent pattern mining (which discovers interesting knowledge in the forms of frequently occurring sets of merchandise items or events) Most of these algorithms use the MapReduce model which mines the search space with distributed or parallel computing (Shim, 2012) Among different big data analysis and mining tasks, this chapter focuses on applying the MapReduce model to big data for the discovery of frequent patterns. BACKGROUND