Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model

High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory of a single machine. In this article, we first develop a parallel and distributed three-stage MapReduce model for mining high-utility sequential patterns based on large-scale databases. Two properties are then developed to hold the correctness and completeness of the discovered patterns in the developed framework. In addition, two data structures called sidset and utility-linked list are utilized in the developed framework to accelerate the computation for mining the required patterns. From the results, we can observe that the designed model has good performance in large-scale datasets in terms of runtime, memory, efficiency of the number of distributed nodes, and scalability compared to the serial HUSP-Span approach.acceptedVersio

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

Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments

In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since current basket-market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high-utility itemsets) in a large-scale database especially with IoT situations. First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases. The -means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3-tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR-Miner compared to the well-known and state-of-the-art CLS-Miner. We present our in-depth results to show that the developed GMR-Miner outperforms CLS-Miner in many criteria, i.e., memory usage, scalability, and runtime.publishedVersio

Frequent itemset mining in big data with effective single scan algorithms

© 2013 IEEE. This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on Hadoop clusters (MR-SSFIM). EA-SSFIM and MR-SSFIM target sparse and big databases, respectively. The proposed approach (in all its variants) requires only one scan to extract the candidate itemsets, and it has the advantage to generate a fixed number of candidate itemsets independently from the value of the minimum support. This accelerates the scan process compared with existing approaches while dealing with sparse and big databases. Numerical results show that SSFIM outperforms the state-of-the-art FIM approaches while dealing with medium and large databases. Moreover, EA-SSFIM provides similar performance as SSFIM while considerably reducing the runtime for large databases. The results also reveal the superiority of MR-SSFIM compared with the existing HPC-based solutions for FIM using sparse and big databases

Approximate Parallel High Utility Itemset Mining

High utility itemset mining discovers itemsets whose utility is above a given threshold, where utilities measure the importance of itemsets. In high utility itemset mining, memory and time performance limitations cause scalability issues, when the dataset is very large. In this thesis, the problem is addressed by proposing a distributed parallel algorithm, PHUI-Miner, and a sampling strategy, which can be used either separately or simultaneously. PHUI-Miner parallelizes the state-of-the-art high utility itemset mining algorithm HUI-Miner. The sampling strategy investigates the required sample size of a dataset, in order to achieve a given accuracy. We also propose an approach combining sampling with PHUI-Miner, which provides better time performance. In our experiments, we show that PHUI-Miner has high performance and outperforms the state-of-the-art non-parallel algorithm. The sampling strategy achieves accuracies much higher than the guarantee. Extensive experiments are also conducted to compare the time performance of PHUI-Miner with and without sampling

Exploring Decomposition for Solving Pattern Mining Problems

This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.publishedVersio

Efficient chain structure for high-utility sequential pattern mining

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates

Frequent Itemset Mining for Big Data

Traditional data mining tools, developed to extract actionable knowledge from data, demonstrated to be inadequate to process the huge amount of data produced nowadays. Even the most popular algorithms related to Frequent Itemset Mining, an exploratory data analysis technique used to discover frequent items co-occurrences in a transactional dataset, are inefficient with larger and more complex data. As a consequence, many parallel algorithms have been developed, based on modern frameworks able to leverage distributed computation in commodity clusters of machines (e.g., Apache Hadoop, Apache Spark). However, frequent itemset mining parallelization is far from trivial. The search-space exploration, on which all the techniques are based, is not easily partitionable. Hence, distributed frequent itemset mining is a challenging problem and an interesting research topic. In this context, our main contributions consist in an (i) exhaustive theoretical and experimental analysis of the best-in-class approaches, whose outcomes and open issues motivated (ii) the development of a distributed high-dimensional frequent itemset miner. The dissertation introduces also a data mining framework which takes strongly advantage of distributed frequent itemset mining for the extraction of a specific type of itemsets (iii). The theoretical analysis highlights the challenges related to the distribution and the preliminary partitioning of the frequent itemset mining problem (i.e. the search-space exploration) describing the most adopted distribution strategies. The extensive experimental campaign, instead, compares the expectations related to the algorithmic choices against the actual performances of the algorithms. We run more than 300 experiments in order to evaluate and discuss the performances of the algorithms with respect to different real life use cases and data distributions. The outcomes of the review is that no algorithm is universally superior and performances are heavily skewed by the data distribution. Moreover, we were able to identify a concrete lack as regards frequent pattern extraction within high-dimensional use cases. For this reason, we have developed our own distributed high-dimensional frequent itemset miner based on Apache Hadoop. The algorithm splits the search-space exploration into independent sub-tasks. However, since the exploration strongly benefits of a full-knowledge of the problem, we introduced an interleaving synchronization phase. The result is a trade-off between the benefits of a centralized state and the ones related to the additional computational power due to parallelism. The experimental benchmarks, performed on real-life high-dimensional use cases, show the efficiency of the proposed approach in terms of execution time, load balancing and reliability to memory issues. Finally, the dissertation introduces a data mining framework in which distributed itemset mining is a fundamental component of the processing pipeline. The aim of the framework is the extraction of a new type of itemsets, called misleading generalized itemsets

Extraction of High Utility Itemsets using Utility Pattern with Genetic Algorithm from OLTP System

To analyse vast amount of data, Frequent pattern mining play an important role in data mining. In practice, Frequent pattern mining cannot meet the challenges of real world problems due to items differ in various measures. Hence an emerging technique called Utility-based data mining is used in data mining processes.The utility mining not only considers the frequency but also see the utility associated with the itemsets.The main objective of utility mining is to extract the itemsets with high utilities, by considering user preferences such as profit,quantity and cost from OLTP systems. In our proposed approach, we are using UP growth with Genetic Algorithm. The idea is that UP growth algorithm would generate Potentially High Utility Itemsets and Genetic Algorithm would optimize and provide the High Utility Item set from it. On comparing with existing algorithm, the proposed approach is performing better in terms of memory utilization. DOI: 10.17762/ijritcc2321-8169.15039