Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, ...

N-list is a novel data structure proposed in recent years. It has been proven to be very efficient for mining frequent itemsets. In this paper, we present PrePost + , a high-performance algorithm for mining frequent itemsets. It employs N-list to represent itemsets and directly discovers frequent itemsets using a set-enumeration search tree. Especially, it employs an efficient pruning strategy ...

Discovering association rules by identifying relationships among sets of items in a transaction database is an important problem in Data Mining. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we describe a more efficient algorithm for mining complete frequent itemset...

Frequent Pattern Mining is a one field of the most significant topics in data mining. In recent years, many algorithms have been proposed for mining frequent itemsets. A new algorithm has been presented for mining frequent itemsets based on N-list data structure called Prepost algorithm. The Prepost algorithm is enhanced by implementing compact PPC-tree with the general tree. Prepost algorithm ...

Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm called ABBM is proposed ...

Frequent itemset mining has become a popular research area in data mining community since the last few years. There are two main technical hitches while finding frequent itemsets. First, to provide an appropriate minimum support value to start and user need to tune this minimum support value by running the algorithm again and again. Secondly, generated frequent itemsets are mostly numerous and ...

Finding Association Rules is a classical data mining task. The most critical part of Association Rules Mining is finding the frequent itemsets in the database. Since the introduce of the famouse Apriori algorithm [14], many others have been proposed to find the frequent itemsets. Among all the algorithms, the approach of mining closed itemsets has arisen a lot of interests in data mining commun...

A complete set of frequent itemsets can get undesirably large due to redundancy when the minimum support threshold is low or when the database is dense. Several concise representations have been proposed to eliminate the redundancy. Existing generator based representations rely on a negative border to make the representation lossless. However, negative borders of generators are often very large...

The aim of this paper is to develop a new mining algorithm to mine all frequent itemsets from a transaction database called the vertical index list (VIL) tree algorithm. The main advantages of the previous algorithms, which are frequent pattern (FP) growth and inverted index structure (IIS) mine, are still useful in a new approach as database scanning only done once, and all frequent itemsets a...

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. It is an extension of the frequent pattern mining. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets ...