Abstract: Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: The various types of frequent pattern discovery
problem, namely, the frequent itemset, sequence and graph mining
problems are solved in different ways which are, however, in certain
aspects similar. The main approach of discovering such patterns can
be classified into two main classes, namely, in the class of the levelwise
methods and in that of the database projection-based methods.
The level-wise algorithms use in general clever indexing structures
for discovering the patterns. In this paper a new approach is proposed
for discovering frequent sequences and tree-like patterns efficiently
that is based on the level-wise issue. Because the level-wise
algorithms spend a lot of time for the subpattern testing problem, the
new approach introduces the idea of using automaton theory to solve
this problem.
Abstract: The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.
Abstract: In this paper, we propose an adaptation of the Patricia-Tree for sparse datasets to generate non redundant rule associations. Using this adaptation, we can generate frequent closed itemsets that are more compact than frequent itemsets used in Apriori approach. This adaptation has been experimented on a set of datasets benchmarks.
Abstract: This paper investigates the problem of sampling from transactional data streams. We introduce CFISDS as a content based sampling algorithm that works on a landmark window model of data streams and preserve more informed sample in sample space. This algorithm that work based on closed frequent itemset mining tasks, first initiate a concept lattice using initial data, then update lattice structure using an incremental mechanism.Incremental mechanism insert, update and delete nodes in/from concept lattice in batch manner. Presented algorithm extracts the final samples on demand of user. Experimental results show the accuracy of CFISDS on synthetic and real datasets, despite on CFISDS algorithm is not faster than exist sampling algorithms such as Z and DSS.
Abstract: The increasing importance of data stream arising in a
wide range of advanced applications has led to the extensive study of
mining frequent patterns. Mining data streams poses many new
challenges amongst which are the one-scan nature, the unbounded
memory requirement and the high arrival rate of data streams. In this
paper, we propose a new approach for mining itemsets on data
stream. Our approach SFIDS has been developed based on FIDS
algorithm. The main attempts were to keep some advantages of the
previous approach and resolve some of its drawbacks, and
consequently to improve run time and memory consumption. Our
approach has the following advantages: using a data structure similar
to lattice for keeping frequent itemsets, separating regions from each
other with deleting common nodes that results in a decrease in search
space, memory consumption and run time; and Finally, considering
CPU constraint, with increasing arrival rate of data that result in
overloading system, SFIDS automatically detect this situation and
discard some of unprocessing data. We guarantee that error of results
is bounded to user pre-specified threshold, based on a probability
technique. Final results show that SFIDS algorithm could attain
about 50% run time improvement than FIDS approach.