scholarly journals MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support

2019 ◽  
Vol 9 (10) ◽  
pp. 2075 ◽  
Author(s):  
Chen-Shu Wang ◽  
Jui-Yen Chang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Pattern Mining ◽  
High Efficiency ◽  
Big Data Analytics ◽  
Frequent Pattern Mining ◽  
Experimental Results ◽  
Frequent Pattern ◽  
Multiple Item ◽  
Two Phases

In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). The proposed architecture consists of two phases. First, in the counting support phase, a Hadoop MapReduce architecture is employed to determine the support for each item. Next, in the analytics phase, sub-transaction blocks are generated according to MIS and the MISFP-growth algorithm identifies the frequency of patterns. To facilitate decision makers in setting MIS, we also propose the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. Three experiments were implemented to validate the proposed Hadoop-based MISFP-growth algorithm. The experimental results show approximately 38% reduction in the execution time on parallel architectures. The proposed MISFP-growth algorithm can be implemented on the distributed computing framework. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications.

Big Data Analytics and Mining for Knowledge Discovery

2021 ◽  
pp. 1817-1830
Author(s):  
Carson K. Leung
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Pattern Mining ◽  
Real Life ◽  
Big Data Analytics ◽  
Organizational Knowledge ◽  
Frequent Pattern ◽  
Data Sets ◽  
Technology Applications ◽  
Rich Data

Big data analytics and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big data sets that contain huge volumes of valuable veracious data collected or generated at a high velocity from a wide variety of rich data sources. Among different big data analytic and mining tasks, this chapter focuses on frequent pattern mining. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover (organizational) knowledge from (i) big data sets of precise data in a breadth-first manner or depth-first manner and/or from (ii) big data sets of uncertain data. Such a big data analytics process can be sped up by focusing the mining according to the user-specified constraints that express the user interests. The resulting (constrained or unconstrained) frequent patterns mined from big data sets provide users with new insights and a sound understanding of users' patterns. Such (organizational) knowledge is useful is many real-life information science and technology applications.

scholarly journals Big Data Frequent Pattern Mining

2014 ◽  
pp. 225-259 ◽  
Author(s):  
David C. Anastasiu ◽  
Jeremy Iverson ◽  
Shaden Smith ◽  
George Karypis
Keyword(s):  
Big Data ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern

Apriori-based High Efficiency Load Balancing Parallel Data Mining Algorithms on Multi-core Architectures

2015 ◽  
Vol 7 (2) ◽  
pp. 77-99 ◽  
Author(s):  
Kun-Ming Yu ◽  
Sheng-Hui Liu ◽  
Li-Wei Zhou ◽  
Shu-Hao Wu
Keyword(s):  
Data Mining ◽  
Load Balancing ◽  
Pattern Mining ◽  
High Efficiency ◽  
Computation Time ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Parallel Data ◽  
Parallel Data Mining ◽  
Mining Methods

Frequent pattern mining has been playing an essential role in knowledge discovery and data mining tasks that try to find usable patterns from databases. Efficiency is especially crucial for an algorithm in order to find frequent itemsets from a large database. Numerous methods have been proposed to solve this problem, such as Apriori and FP-growth. These are regarded as fundamental frequent pattern mining methods. In addition, parallel computing architectures, such as an on-cloud platform, a grid system, multi-core and GPU platform, have been popular in data mining. However, most of the algorithms have been proposed without considering the prevalent multi-core architectures. In this study, multi-core architectures were used as well as two high efficiency load balancing parallel data mining methods based on the Apriori algorithm. The main goal of the proposed algorithms was to reduce the massive number of duplicate candidates generated using previous methods. This goal was achieved for, in this detailed experimental study the algorithms performed better than the previous methods. The experimental results demonstrated that the proposed algorithms had dramatically reduced computation time when using more threads. Moreover, the observations showed that the workload was equally balanced among the computing units.

scholarly journals An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 555
Author(s):  
Linlin Jia ◽  
Laisheng Xiang ◽  
Xiyu Liu
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Experimental Results ◽  
Frequent Pattern ◽  
P System ◽  
Vertical Data ◽  
Active Membranes ◽  
Pruning Strategy ◽  
Mining Algorithms ◽  
Rewriting Rules

The Eclat algorithm is a typical frequent pattern mining algorithm using vertical data. This study proposes an improved Eclat algorithm called ETPAM, based on the tissue-like P system with active membranes. The active membranes are used to run evolution rules, i.e., object rewriting rules, in parallel. Moreover, ETPAM utilizes subsume indices and an early pruning strategy to reduce the number of frequent pattern candidates and subsumes. The time complexity of ETPAM is decreased from O(t2) to O(t) as compared with the original Eclat algorithm through the parallelism of the P system. The experimental results using two databases indicate that ETPAM performs very well in mining frequent patterns, and the experimental results using four databases prove that ETPAM is computationally very efficient as compared with three other existing frequent pattern mining algorithms.

scholarly journals Frequent Pattern Mining over Unstructured Data using Semi-Structured Doc-Model and Pattern Ranking

2020 ◽  
pp. 36-42
Author(s):  
Sudhir Tirumalasetty ◽  
A. Divya ◽  
D. Rahitya Lakshmi ◽  
Ch. Durga Bhavani ◽  
D. Anusha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Unstructured Data ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Innovative Methods ◽  
Mining Algorithms ◽  
Doc Model

Frequent pattern mining is an essential data-mining task, with a goal of discovering knowledge in the form of repeated patterns. Many efficient pattern-mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data we are presented with today, the so-called “Big Data”. Scalable parallel algorithms hold the key to solving the problem in this context. This paper reviews recent advances in parallel frequent pattern mining, analysing them through the Big Data lens. Load balancing and work partitioning are the major challenges to be conquered. These challenges always invoke innovative methods to do, as Big Data evolves with no limits. The biggest challenge than before is conquering unstructured data for finding frequent patterns. To accomplish this Semi Structured Doc-Model and ranking of patterns are used.

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