145,466 research outputs found
Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems
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 internal layout for transactions and itemsets. This technique reduces
the memory space for storing the transactional database, simplifies the support
count via logical bitwise operation, and allows for vectorization of such a
step. Experimental evaluation on the platforms of the Intel Xeon CPU and the
Intel Xeon Phi coprocessor with large synthetic and real databases showed good
performance and scalability of the proposed algorithm.Comment: Accepted for publication in Journal of Computing and Information
Technology (http://cit.fer.hr
Discovering unbounded episodes in sequential data
One basic goal in the analysis of time-series data is
to find frequent interesting episodes, i.e, collections
of events occurring frequently together in the input sequence.
Most widely-known work decide the interestingness of an episode from a
fixed user-specified window width or interval, that bounds the
subsequent sequential association rules.
We present in this paper, a more intuitive definition that
allows, in turn, interesting episodes to grow during the mining without any
user-specified help. A convenient algorithm to
efficiently discover the proposed unbounded episodes is also implemented.
Experimental results confirm that our approach results useful
and advantageous.Postprint (published version
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
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