Browsing by Subject "Parallel data mining"
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Item Open Access Efficient parallel frequency mining based on a novel top-down partitioning scheme for transactional data(2002) Özkural, ErayIn recent years, large quantities of data have been amassed with advances in data acquisition capabilities. Automated detection of useful information is required for vast data obtained from scientific and business domains. Data Mining is the application of efficient algorithmic solutions on a variety of immense data for such knowledge discovery. Frequency mining discovers all frequent patterns in a transaction or relational database and it comprises the core of several data mining algorithms such as association rule mining and sequence mining. Frequent pattern discovery has become a challenge for parallel programming since it is a highly complex operation on huge datasets demanding efficient and scalable algorithms. In this thesis, we propose a new family of parallel frequency mining algo rithms. We introduce a novel transaction set partitioning scheme that can be used to divide the frequency mining task in a top-down fashion. The method op erates on the graph of frequent patterns with length two (Gp2) from which a graph partitioning by vertex separator (GPVS) is mapped to a two-way partitioning on the transaction set. The two parts obtained can be mined independently and therefore can be utilized for concurrency. In order for this property to hold, there is an amount of replication dictated by the separator in Gp2 which is minimized by the GPVS algorithm. A k-way partitioning is derived from recursive applica tion of 2- way partitioning scheme which is used in the design of a generic parallel frequency mining algorithm. First we compute Gp2 in parallel, succeeding that we designate a k-way partitioning of the database for k processors with a parallel IVrecursive procedure. The database is redistributed such, that each processor is as signed one part. Subsequent mining proceeds simultaneously and independently at each processor with a given serial mining algorithm. A complete implemen tation in which we employ FP- Growth as the sequential algorithm has been achieved. The performance study of the algorithm on a Beowulf system demon strates favorable performance for synthetic databases. For hard instances of the problem, we have gained approximately twice the speedup of a state-of-the-art algorithm. We also present a correction and optimization to FP- Growth algorithm.Item Open Access Parallel frequent item set mining with selective item replication(Institute of Electrical and Electronics Engineers, 2011) Özkural E.; Uçar, B.; Aykanat, CevdetWe introduce a transaction database distribution scheme that divides the frequent item set mining task in a top-down fashion. Our method operates on a graph where vertices correspond to frequent items and edges correspond to frequent item sets of size two. We show that partitioning this graph by a vertex separator is sufficient to decide a distribution of the items such that the subdatabases 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. © 2011 IEEE.