Big Data Analysis and Mining

Big Data Analysis and Mining

Carson K.-S. Leung (University of Manitoba, Canada)
DOI: 10.4018/978-1-5225-7598-6.ch002
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Big data analysis and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big databases that contain high volumes of valuable veracious data collected or generated at a high velocity from a wide variety of data sources. Among different big data mining tasks, this chapter focuses on big data analysis and mining for frequent patterns. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover frequent patterns from (1) big databases of precise data in a breadth-first manner or in a depth-first manner and/or from (2) big databases of uncertain data. Such a big data analysis and mining process can be sped up. The resulting (constrained or unconstrained) frequent patterns mined from big databases provide users with new insights and a sound understanding of users' patterns. Such knowledge is useful is many real-life information science and technology applications.
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Since the introduction of the research problem of frequent pattern mining (Agrawal, Imieliński, & Swami, 1993), numerous algorithms have been proposed (Hipp, Güntzer, & Nakhaeizadeh, 2000; Ullman, 2000; Ceglar & Roddick, 2006). Notable ones include the classical Apriori algorithm (Agrawal & Srikant, 1994) and its variants such as the Partition algorithm (Savasere, Omiecinski, & Navathe, 1995). The Apriori algorithm uses a level-wise breadth-first bottom-up approach with a candidate generate-and-test paradigm to mine frequent patterns from transactional databases of precise data. The Partition algorithm divides the databases into several partitions and applies the Apriori algorithm to each partition to obtain patterns that are locally frequent in the partition. As being locally frequent is a necessary condition for a pattern to be globally frequent, these locally frequent patterns are tested to see if they are globally frequent in the databases. To avoid the candidate generate-and-test paradigm, the tree-based FP-growth algorithm (Han, Pei, & Yin, 2000) was proposed. It uses a depth-first pattern-growth (i.e., divide-and-conquer) approach to mine frequent patterns using a tree structure that captures the contents of the databases. Specifically, the algorithm recursively extracts appropriate tree paths to form projected databases containing relevant transactions and to discover frequent patterns from these projected databases.

In various real-life business, engineering, scientific applications in modern organizations and society, the available data are not precise data but uncertain data (Tong et al., 2012; Leung, Cuzzocrea, & Jiang, 2013; Leung, MacKinnon & Tanbeer, 2014; Jiang & Leung, 2015; Ahmed et al., 2016). Examples include sensor data and privacy-preserving data. Over the past few years, several algorithms have been proposed to mine and analyze these uncertain data. The tree-based UF-growth algorithm (Leung, Mateo, & Brajczuk, 2008) is an example.

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