Data mining is the computer-assisted process of information analysis. Mining frequent itemsets is a fundamental task in data mining. Unfortunately the number of frequent itemsets describing the data is often too large to comprehend. This problem has been attacked by condensed representations of frequent itemsets that are sub collections of frequent itemsets containing only the frequent itemsets...

Mining frequent itemsets from transactional data streams is challenging due to the nature of the exponential explosion of itemsets and the limit memory space required for mining frequent itemsets. Given a domain of I unique items, the possible number of itemsets can be up to 2 1. When the length of data streams approaches to a very large number N, the possibility of an itemset to be frequent be...

This paper proposes a new strategy for maintaining association rules in dynamic databases. This method uses weighting technique to highlight new data. Our approach is novel in that recently added transactions are given higher weights. In particular, we look at how frequent itemsets can be maintained incrementally. We propose a competitive model to ‘promote’ infrequent itemsets to frequent items...

Association rule mining among frequent items has been widely studied in data mining field. Many researches have improved the algorithm for generation of all the frequent itemsets. In this paper, we proposed a new algorithm to mine all frequents itemsets from a transaction database. The main features of this paper are: (1) the database is scanned only one time to mine frequent itemsets; (2) the ...

Finding all closed frequent itemsets is a key step of association rule mining since the non-redundant association rule can be inferred from all the closed frequent itemsets. In this paper we present a new method for finding closed frequent itemsets based on attribute value lattice. In the new method, we argue that vertical data representation and attribute value lattice can find all closed freq...

Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently several proposals have been made to construct a concise representation of the frequent itemsets, ins...

<p><span>Mining frequent itemsets is an area of data mining that has beguiled several researchers in recent years. Varied structures such as Nodesets, DiffNodesets, NegNodesets, N-lists, and Diffsets are among a few were employed to extract items. However, most these approaches fell short either respect run time or memory. Hybrid frameworks formulated repress issues encompass the de...

Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional itemset mining. This is because the AFI algorithms generate large number of candidate itemsets. article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution proposed approach it mines core patterns and examines approximate conditions directly wi...