An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-pattern tree, or FPtree, which retains the itemset association information. It then divides the compressed database into a...

We propose an algorithm that computes an approximation of the set of frequent item sets by using the bit sequence representation of the associations between items and transactions. The algorithm is obtained by modifying a hierarchical agglomerative clustering algorithm and takes advantage of the speed that bit operations afford. The algorithm offers a very significant speed advantage over stand...

Many frequent pattern mining algorithms find patterns from traditional transaction databases, in which the content of each transaction—namely, items—is definitely known and precise. However, there are many real-life situations in which the content of transactions is uncertain. To deal with these situations, we propose a tree-based mining algorithm to efficiently find frequent patterns from unce...

: Association rule mining is the most popular technique in data mining. Mining association rules is a prototypical problem as the data are being generated and stored every day in corporate computer database systems. To manage this knowledge, rules have to be pruned and grouped, so that only reasonable numbers of rules have to be inspected and analyzed. In this paper we compare the standard asso...

Frequent itemsets are one of the best known concepts in data mining, and there is active research in itemset mining algorithms. An itemset is frequent in a database if its items co-occur in sufficiently many records. This thesis addresses two questions related to frequent itemsets. The first question is raised by a method for approximating logical queries by an inclusion-exclusion sum truncated...

Data items have been extracted using an empirical data mining technique called frequent itemset mining. In majority of theapplication contexts items are enriched with weights. Pushing an item weights into the itemset extraction process, i.e., mining weighted itemsets rather than traditional itemsets, is an appealing research direction. Although many efficient weighteditemset mining algorithms a...

Discovering association rules that identify relationships among sets of items is an important problem in data mining. It’s a two steps process, the first step finds all frequent itemsets and the second one constructs association rules from these frequent sets. Finding frequent itemsets is computationally the most expensive step in association rules discovery algorithms. Utilizing parallel archi...

Association rules are the main technique to determine the frequent itemset in data mining. Apriori algorithm is a classical algorithm of association rule mining. This classical algorithm is inefficient due to so many scans of database. And if the database is large, it takes too much time to scan the database. In this paper, we proposed an Improved Apriori algorithm which reduces the scanning ti...