Observations on Factors Affecting Performance of MapReduce based Apriori on Hadoop Cluster

Designing fast and scalable algorithm for mining frequent itemsets is always being a most eminent and promising problem of data mining. Apriori is one of the most broadly used and popular algorithm of frequent itemset mining. Designing efficient algorithms on MapReduce framework to process and analyze big datasets is contemporary research nowadays. In this paper, we have focused on the performance of MapReduce based Apriori on homogeneous as well as on heterogeneous Hadoop cluster. We have investigated a number of factors that significantly affects the execution time of MapReduce based Apriori running on homogeneous and heterogeneous Hadoop Cluster. Factors are specific to both algorithmic and non-algorithmic improvements. Considered factors specific to algorithmic improvements are filtered transactions and data structures. Experimental results show that how an appropriate data structure and filtered transactions technique drastically reduce the execution time. The non-algorithmic factors include speculative execution, nodes with poor performance, data locality & distribution of data blocks, and parallelism control with input split size. We have applied strategies against these factors and fine tuned the relevant parameters in our particular application. Experimental results show that if cluster specific parameters are taken care of then there is a significant reduction in execution time. Also we have discussed the issues regarding MapReduce implementation of Apriori which may significantly influence the performance.Comment: 8 pages, 8 figures, International Conference on Computing, Communication and Automation (ICCCA2016

RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD Framework

Initially, a number of frequent itemset mining (FIM) algorithms have been designed on the Hadoop MapReduce, a distributed big data processing framework. But, due to heavy disk I/O, MapReduce is found to be inefficient for such highly iterative algorithms. Therefore, Spark, a more efficient distributed data processing framework, has been developed with in-memory computation and resilient distributed dataset (RDD) features to support the iterative algorithms. On the Spark RDD framework, Apriori and FP-Growth based FIM algorithms have been designed, but Eclat-based algorithm has not been explored yet. In this paper, RDD-Eclat, a parallel Eclat algorithm on the Spark RDD framework is proposed with its five variants. The proposed algorithms are evaluated on the various benchmark datasets, which shows that RDD-Eclat outperforms the Spark-based Apriori by many times. Also, the experimental results show the scalability of the proposed algorithms on increasing the number of cores and size of the dataset.Comment: 16 pages, 6 figures, ICCNCT 201

CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON MAPREDUCE FRAMEWORK

Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies are advancing and people uses these technologies in day to day activities, this data is termed as Big Data having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets but it has large communication cost which reduces execution efficiency. This proposed new pre-processed k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets from generated clusters using MapReduce programming model. Results shown that execution efficiency of ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as one of the pre-processing technique

A Novel Nodesets-Based Frequent Itemset Mining Algorithm for Big Data using MapReduce

Due to the rapid growth of data from different sources in organizations, the traditional tools and techniques that cannot handle such huge data are known as big data which is in a scalable fashion. Similarly, many existing frequent itemset mining algorithms have good performance but scalability problems as they cannot exploit parallel processing power available locally or in cloud infrastructure. Since big data and cloud ecosystem overcomes the barriers or limitations in computing resources, it is a natural choice to use distributed programming paradigms such as Map Reduce. In this paper, we propose a novel algorithm known as A Nodesets-based Fast and Scalable Frequent Itemset Mining (FSFIM) to extract frequent itemsets from Big Data. Here, Pre-Order Coding (POC) tree is used to represent data and improve speed in processing. Nodeset is the underlying data structure that is efficient in discovering frequent itemsets. FSFIM is found to be faster and more scalable in mining frequent itemsets. When compared with its predecessors such as Node-lists and N-lists, the Nodesets save half of the memory as they need only either pre-order or post-order coding. Cloudera\u27s Distribution of Hadoop (CDH), a MapReduce framework, is used for empirical study. A prototype application is built to evaluate the performance of the FSFIM. Experimental results revealed that FSFIM outperforms existing algorithms such as Mahout PFP, Mlib PFP, and Big FIM. FSFIM is more scalable and found to be an ideal candidate for real-time applications that mine frequent itemsets from Big Data