In this paper we introduce a new parallel algorithm MLFPT (multiple local frequent pattern
tree) for parallel mining of frequent patterns, based on FP-growth mining, that uses only two …

In this paper, we present a tree-partition algorithm for parallel mining of frequent patterns.
Our work is based on FP-Growth algorithm, which is constituted of tree-building stage and …

Most of studies for mining frequent patterns are based on constructing tree for arranging the
items to mine frequent patterns. Many algorithms proposed recently have been motivated by …

Discovery of association rules among the large number of item sets is considered as an
important aspect of data mining. The ever increasing demand of finding pattern from large …

Extraction of frequent patterns from a transactional database is a fundamental task in data
mining. Its applications include association rules, time series, etc. The Apriori approach is a …

Mining frequent patterns with an FP-tree avoids costly candidate generation and repeatedly
occurrence frequency checking against the support threshold. It therefore achieves better …

Previous studies propose that associative classification has high classification accuracy and
strong flexibility at handling unstructured data. However, it still suffers from the huge set of …

The frequent itemset mining (FIM) is one of the most important techniques to extract
knowledge from data in many real-world applications. The Apriori algorithm is the widely …

Extraction of frequent patterns in transaction-oriented database is crucial to several data
mining tasks such as association rule generation, time series analysis, classification, etc …

Traditional methods for frequent itemset mining typically assume that data is centralized and
static. Such methods impose excessive communication overhead when data is distributed …