A fast parallel algorithm for frequent itemsets mining (original) (raw)
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A highly parallel algorithm for frequent itemset mining
Advances in Pattern …, 2010
Abstract. Mining frequent itemsets in large databases is a widely used technique in Data Mining. Several sequential and parallel algorithms have been developed, although, when dealing with high data volumes, the execution of those algorithms takes more time and resources ...
A generalized parallel algorithm for frequent itemset mining
A parallel algorithm for finding the frequent itemsets in a set of transactions is presented. The frequent individual items are identified by their index. We assume that processors number (m) is less than the frequent items number (n). At the first stage, every processor Pi, i isin; {1, ...,m - 1} sequentially computes the frequent itemsets from the interval Ii = [(i - 1) cdot; p + 1, i cdot; p], where p = lfloor;n/mrfloor;. The processor Pm computes frequent itemsets from the interval Im = [(m - 1) cdot; p + 1, n]. In the second stage, the parallel algorithm is applied. The processor Pi computes, step by step, the sets FIi,Ij of the frequent itemsets with individual items from the intervals Ii,j = Ii∪Ii+1∪...∪Ij, j = i+1,...,m. In order to compute the set FIi,Ij, the processor Pi uses FIi,Ij-1 obtained in the previous step and FIi+1,Ij received from the processor Pi+1. The main advantage of our parallel algorithm is that it uses a communication pattern known before algorithm start,...
New parallel algorithms for frequent itemset mining in very large databases
2003
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of data in order to produce compact summaries or models of the database. These models are typically used to generate association rules, but recently they have also been used in far reaching domains like e-commerce and bio-informatics. Because databases are increasing in terms of both dimension (number of attributes) and size (number of records), one of the main issues in a frequent itemset mining algorithm is the ability to analyze very large databases. Sequential algorithms do not have this ability, especially in terms of run-time performance, for such very large databases. Therefore, we must rely on high performance parallel and distributed computing. We present new parallel algorithms for frequent itemset mining. Their efficiency is proven through a series of experiments on different parallel environments, that range from shared-memory multiprocessors machines to a set of SMP clusters connected together through a high speed network.
Parallel and distributed methods for incremental frequent itemset mining
2004
Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and input/output (I/O) resources when data is dynamic, and they impose excessive communication overhead when data is distributed. Efficient implementation of incremental data mining methods is, thus, becoming crucial for ensuring system scalability and facilitating knowledge discovery when data is dynamic and distributed. In this paper, we address this issue in the context of the important task of frequent itemset mining. We first present an efficient algorithm which dynamically maintains the required information even in the presence of data updates without examining the entire dataset. We then show how to parallelize this incremental algorithm. We also propose a distributed asynchronous algorithm, which imposes minimal communication overhead for mining distributed dynamic datasets. Our distributed approach is capable of generating local models (in which each site has a summary of its own database) as well as the global model of frequent itemsets (in which all sites have a summary of the entire database). This ability permits our approach not only to generate frequent itemsets, but also to generate high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites. Index Terms-Distributed computing, grid computing, incremental data mining, parallel computing. I. INTRODUCTION T HE FIELD of knowledge discovery and data mining (KDD), spurred by advances in data collection technology, is concerned with the process of deriving interesting and useful patterns from large datasets. Frequent itemset mining is a core data mining task. It has an elegantly simple problem statement: to find the set of all subsets of items that frequently occur together in database records or transactions. Although this task has a simple statement, it is CPU and input/output (I/O) intensive, mainly because the large number of itemsets that are typically generated and the large size of the datasets involved in the process.
Parallel Frequent Itemset Mining with Selective Item Replication
IEEE Transactions on Parallel and …, 2011
We introduce a transaction database distribution scheme which divides the frequent itemset mining task in a topdown fashion. Our method operates on a graph where vertices correspond to frequent items and edges correspond to frequent item sets. We show that partitioning this graph by a vertex separator is sufficient to decide a distribution of the items such that the sub-databases determined by the item distribution can be mined independently. This distribution entails an amount of data replication, which may be reduced by setting appropriate weights to vertices. The data distribution scheme is used in the design of two new parallel frequent item set mining algorithms. Both algorithms replicate the items that correspond to the separator. NoClique replicates the work induced by the separator and NoClique2 computes the same work collectively. Computational load balancing and minimization of redundant or collective work may be achieved by assigning appropriate load estimates to vertices. The experiments show favorable speedups on a system with small-to-medium number of processors for synthetic and real-world databases.
Parallel Binary Approach for Frequent Itemsets Mining
The technique of association rules discovering is one of the most known and the most explored techniques of data mining. This technique has two main phases: the first is to extract all the frequent itemsets and the second is to generate association rules from these frequent itemsets. The first phase is the most expensive given the large number of accesses to transactions database and the large number of candidate itemsets. As databases are generally very large, a solution to avoid the repetitive and costly accesses is to represent them by compact structures. In this paper, we propose a parallel binary approach for frequent itemsets extracting, to deal with the great number of candidates and to take advantage of multicore architectures. This approach is implemented using a compact data structure based on signatures tree for the representation of the database to access it only once.
A multithreaded hybrid framework for mining frequent itemsets
International Journal of Electrical and Computer Engineering (IJECE), 2022
Mining frequent itemsets is an area of data mining that has beguiled several researchers in recent years. Varied data structures such as Nodesets, DiffNodesets, NegNodesets, N-lists, and Diffsets are among a few that were employed to extract frequent items. However, most of these approaches fell short either in respect of run time or memory. Hybrid frameworks were formulated to repress these issues that encompass the deployment of two or more data structures to facilitate effective mining of frequent itemsets. Such an approach aims to exploit the advantages of either of the data structures while mitigating the problems of relying on either of them alone. However, limited efforts have been made to reinforce the efficiency of such frameworks. To address these issues this paper proposes a novel multithreaded hybrid framework comprising of NegNodesets and N-list structure that uses the multicore feature of today's processors. While NegNodesets offer a concise representation of itemsets, N-lists rely on List intersection thereby speeding up the mining process. To optimize the extraction of frequent items a hash-based algorithm has been designed here to extract the resultant set of frequent items which further enhances the novelty of the framework.
Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems
Journal of Computing and Information Technology
Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional databases. Apriori is a classical frequent itemset mining algorithm, which employs iterative passes over database combining with generation of candidate itemsets based on frequent itemsets found at the previous iteration, and pruning of clearly infrequent itemsets. The Dynamic Itemset Counting (DIC) algorithm is a variation of Apriori, which tries to reduce the number of passes made over a transactional database while keeping the number of itemsets counted in a pass relatively low. In this paper, we address the problem of accelerating DIC on the Intel Xeon Phi many-core system for the case when the transactional database fits in main memory. Intel Xeon Phi provides a large number of small compute cores with vector processing units. The paper presents a parallel implementation of DIC based on OpenMP technology and thread-level parallelism. We exploit the bit-based intern...
2010 International Conference on High Performance Computing & Simulation, 2010
The problem of closed frequent itemset discovery is a fundamental problem of data mining, having applications in numerous domains. It is thus very important to have efficient parallel algorithms to solve this probem, capable of efficiently harnessing the power of multicore processors that exists in our computers (notebooks as well as desktops). In this paper we present PLCM QS , a parallel algorithm based on the LCM algorithm, recognized as the most efficient algorithm for sequential discovery of closed frequent itemsets. We also present a simple yet powerfull parallelism interface based on the concept of Tuple Space, which allows an efficient dynamic sharing of the work. Thanks to a detailed experimental study, we show that PLCM QS is efficient on both on sparse and dense databases.
Efficient Parallel Mining Of Frequent Itemset Using MapReduce
International Journal of Information Systems and Computer Sciences, 2019
Big dataextremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interaction and the data mining used for dig deep into analyzing the patterns and relationships of data. Frequent item set mining is a data mining method that was developed for market basket analysis. In the project proposed to anefficient data processing using Lshfp growth algorithm and grouping similar objects as the clusters with group id. The traditional datamining is based on the fp growth algorithm focused on the load balancing, and distributed among the nodes of the clusters.The process is mainly based on mapreduce which highly supported by Hadoop.Hadoop is a efficient popular frame work which supports mapreduce and itemset mining .Map reduce is that which contains map phase and reduce phase.Map phase which results the pair of key values and reduce phase which results the reduced results. It aims to decrease network overhead and efficient processing.