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...

-Association rule has been an area of active research in the field of knowledge discovery. Data mining researchers had improved upon the quality of association rule mining for business development by incorporating influential factors like value (utility), quantity of items sold (weight) and more for the mining of association patterns. In this paper, we propose an efficient approach to find maxi...

When companies seek for the combination of products which can constantly generate high profit, the association rule mining (ARM) or the utility mining will not achieve such task. ARM mines frequent itemsets without knowing the producing profit. On the other hand, the utility mining seeks high profit items but no guarantee the frequency. In this paper, we propose a novel utility-frequent mining ...

Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like u...

Association rule is a data mining technique that has a huge number of applications. One of the crucial steps in association rule is the extraction of frequent itemsets. This research is inspired by simple appealing visualization of itemsets frequencies in the simple well known two dimension matrix representations. This paper proposes a new procedure to extract maximal frequent itemsets called M...

Mining frequent closed itemsets in data streams is an important task in stream data mining. In this paper, an efficient mining algorithm (denoted as EMAFCI) for frequent closed itemsets in data stream is proposed. The algorithm is based on the sliding window model, and uses a Bit Vector Table (denoted as BVTable) where the transactions and itemsets are recorded by the column and row vectors res...