Frequent Pattern Mining Using Record Filter Approach
In today's emerging world, the role of data mining is increasing
day by day with the new aspect of business. Data mining has
been proved as a very basic tool in knowledge discovery and
decision making process. Data mining technologies are very
frequently used in a variety of applications. Frequent itemsets
play an essential role in many data mining tasks that try to find
interesting patterns from databases, such as association rules,
correlations, sequences, episodes, classifiers, clusters. Frequent
patterns are the itemsets that are frequently visited in database
transactions at least for the user defined number of times which
is known as support threshold. Presently a number of algorithms
have been proposed in literature to enhance the performance of
Apriori Algorithm, for the purpose of determining the frequent
pattern. The main issue for any algorithm is to reduce the
processing time. Present paper proposes a new record filtering
based approach which takes very less time for performing
computations during mining process. Experiments have been
performed on synthetic datasets and the results have been
presented. The results show that proposed approach performs
well in terms of execution time and ultimately enhances
efficiency as compared to traditional Apriori approach.
Keywords: Association Rule, Apriori, Frequent Patterns, Record
Filtering
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