One of the most popular data mining approach to find frequent itemset in a given transactional dataset is Association rule mining. The important task of Association rule mining is to mine association rules using minimum support value which is specified by the user or can be generated by system itself. In order to calculate minimum support value, every time the complete database has to be scanne...

Data Mining techniques are helpful to uncover the hidden predictive patterns from large masses of data. Frequent item set mining also called Market Basket Analysis is one the most famous and widely used data mining technique for finding most recurrent itemsets in large sized transactional databases. Many methods are devised by researchers in this field to carry out this task, some of these are ...

In standard frequent item set mining one tries to find item sets the support of which exceeds a user-specified threshold (minimum support) in a database of transactions. We, instead, strive to find item sets for which the similarity of the covers of the items (that is, the sets of transactions containing the items) exceeds a user-defined threshold. This approach yields a much better assessment ...

Deletion of transactions in databases is common in real-world applications. Developing an efficient and effective mining algorithm to maintain discovered information is thus quite important in data mining fields. A lot of algorithms have been proposed in recent years, and the best of them is the pre-large-tree-based algorithm. However, this algorithm only rebuilds the final pre-large tree every...

Big data mining methods supports knowledge discovery on high scalable, high volume and high velocity data elements. The cloud computing environment provides computational and storage resources for the big data mining process. Hadoop is a widely used parallel and distributed computing platform for big data analysis and manages the homogeneous and heterogeneous computing models. The MapReduce fra...

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 ...

this study investigated the utility of all of the above (aota) as a test option in multiple-choice items. it aimed at estimating item fit, item difficulty, item discrimination, and guess factor of such a choice. five reading passages of the key english test (ket, 2010) were adapted. the test was reconstructed in 2 parallel forms: test 1 did not include the abovementioned alternative, whereas te...

Frequent patterns are patterns such as item sets, subsequences or substructures that appear in a data set frequently. A Divide and Conquer method is used for finding frequent item set mining. Its core advantages are extremely simple data structure and processing scheme. Divide the original dataset in the projected database and find out the frequent pattern from the dataset. Split and Merge uses...

Frequent Itemset Mining is evaluating the rules and relationship within the data items are optimizing it, in the large spatial databases (for e.g. Images, Docs, AVI files etc).It is one of the major problems in DM (Data mining) domain. Finding frequent item set in the large set is one of the computational complexities in mining. To improve the efficiency and performance of the mining frequent i...

Construction and development of classifier that works with more accuracy and performs efficiently for large database is one of the key tasks of data mining techniques. Secondly training dataset repeatedly produces massive amount of rules. It’s very tough to store, retrieve, prune, and sort a huge number of rules proficiently before applying to a classifier. In such situation FP is the best choi...