Itemset share has been proposed as a measure of the importance of itemsets for mining association rules. The value of the itemset share can provide useful information such as total profit or total customer purchased quantity associated with an itemset in database. The discovery of share-frequent itemsets does not have the downward closure property. Existing algorithms for discovering share-freq...

History Generalized Algorithm is used in this paper which is used to find the generalized as well as non generalized itemsets .after finding those itemsets find out the particular node in the tree structure and attach those itemsets in specific order. One dynamic pattern, the history generalized pattern ,that represents the development of an itemset in successive time periods, by accounting the...

While frequent pattern mining is fundamental for many data mining tasks, mining maximal frequent itemsets efficiently is important in both theory and applications of frequent itemset mining. The fundamental challenge is how to search a large space of item combinations. Most of the existing methods search an enumeration tree of item combinations in a depthfirst manner. In this thesis, we develop...

Frequent itemset mining attempts to find frequent subsets in a transaction database. In this era of big data, demand for frequent itemset mining is increasing. Therefore, the combination of fast implementation and low memory consumption, especially for stream data, is needed. In response to this, we optimize an online algorithm, called Skip LC-SS algorithm [1], for hardware. In this paper, we p...

The Itemset Tree is an efficient data structure for performing targeted queries for itemset mining and association rule mining. It is incrementally updatable by inserting new transactions and it provides efficient querying and updating algorithms. However, an important limitation of the IT structure, concerning scalability, is that it consumes a large amount of memory. In this paper, we address...

Frequent itemset mining can be regarded as advanced database querying where a user specifies the dataset to be mined and constraints to be satisfied by the discovered itemsets. One of the research directions influenced by the above observation is the processing of sets of frequent itemset queries operating on overlapping datasets. Several methods of solving this problem have been proposed, all ...

Computing frequent itemsets is one of the most prominent problems in data min-ing. We study the following related problem, called FREQSAT, in depth: given someitemset-interval pairs, does there exist a database such that for every pair the fre-quency of the itemset falls into the interval? This problem is shown to be NP-complete. The problem is then further extended to inclu...

A core problem in data mining is enumerating frequentlyoccurring itemsets in a given set of transactions. The search and enumeration versions of this problem have recently been proven NP and #P -hard, respectively (Gunopulos et al, 2003) and known algorithms all have running times whose exponential terms are functions of either the size of the largest transaction in the input and/or the largest...

An itemset in traditional utility mining only considers individual profits and quantities of items in transactions but not its itemset length. The average-utility measure, which is the total utility of an itemset divided by its number of items within it, was then proposed to reveal a better utility effect than the original utility one. However, their proposed approach was based on the principle...